{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Day13 进阶构图元素——图例篇（中）\n",
    "\n",
    "　　在昨天的日程里我们了解到在`matplotlib`中图例是如何与图层进行绑定和显示的，今天我们接着前面的铺垫，来学习在`matplotlib`中有关图例设置的常用参数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-28T11:26:54.288956Z",
     "iopub.status.busy": "2020-10-28T11:26:54.287959Z",
     "iopub.status.idle": "2020-10-28T11:26:54.609077Z",
     "shell.execute_reply": "2020-10-28T11:26:54.608078Z",
     "shell.execute_reply.started": "2020-10-28T11:26:54.288956Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "matplotlib版本： 3.3.2\n"
     ]
    }
   ],
   "source": [
    "import matplotlib;print('matplotlib版本：', matplotlib.__version__)\n",
    "import matplotlib.pyplot as plt # 从matplotlib中导入我们最经常使用的pyplot子模块\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei'] # 设定默认字体为SimHei以解决中文显示乱码问题\n",
    "plt.rcParams['axes.unicode_minus'] = False # 解决图像负号'-'不正常显示的问题"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "　　为了方便后面的参数演示，我们先来创建一个包含了散点、折线以及柱状图的图像，并像昨天介绍的那样配上`label`参数："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-28T11:26:54.611073Z",
     "iopub.status.busy": "2020-10-28T11:26:54.610073Z",
     "iopub.status.idle": "2020-10-28T11:26:54.864374Z",
     "shell.execute_reply": "2020-10-28T11:26:54.864374Z",
     "shell.execute_reply.started": "2020-10-28T11:26:54.611073Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "\n",
    "x = np.array(range(5))\n",
    "\n",
    "ax.scatter(x, [5]*5, label='散点图')\n",
    "\n",
    "ax.plot(x, [4]*5, label='折线图')\n",
    "\n",
    "ax.bar(x-0.25, [3]*5, width=0.5, label='柱状图1')\n",
    "ax.bar(x+0.25, [3]*5, width=0.5, label='柱状图2')\n",
    "\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "　　关于`legend()`的常用参数，`handles`与`labels`前面已经介绍过不用多说，下面我们来学习常用的其他重要参数："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **loc**\n",
    "\n",
    "　　在`legend()`中我们可以利用参数`loc`来设置图例的位置，有两种参数格式，先来看看第一种：\n",
    "\n",
    "　　第一种格式下`loc`参数接受一个用于描述其在绘图区域内方位的字符串，格式统一为`'竖直位置 水平位置'`，竖直位置有`'upper'`、`'center'`以及`'lower'`三种，水平位置有`'left'`、`'center'`和`'right'`三种，譬如下面我们使用`lower right`就代表将图例置于绘图区域的右下角："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-28T11:26:54.865370Z",
     "iopub.status.busy": "2020-10-28T11:26:54.865370Z",
     "iopub.status.idle": "2020-10-28T11:26:54.994018Z",
     "shell.execute_reply": "2020-10-28T11:26:54.994018Z",
     "shell.execute_reply.started": "2020-10-28T11:26:54.865370Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 使用loc参数调整图例的位置\n",
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "\n",
    "ax.scatter(x, [5]*5, label='散点图')\n",
    "\n",
    "ax.plot(x, [4]*5, label='折线图')\n",
    "\n",
    "ax.bar(x-0.25, [3]*5, width=0.5, label='柱状图1')\n",
    "ax.bar(x+0.25, [3]*5, width=0.5, label='柱状图2')\n",
    "\n",
    "ax.legend(loc='lower right');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "　　第二种格式下`loc`参数接受一个二元组，分别控制图例左下角所在的x与y位置，譬如`(1, 1)`即将图例的左下角置于绘图区域的右上角："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-28T11:26:54.995050Z",
     "iopub.status.busy": "2020-10-28T11:26:54.995050Z",
     "iopub.status.idle": "2020-10-28T11:26:55.125690Z",
     "shell.execute_reply": "2020-10-28T11:26:55.125690Z",
     "shell.execute_reply.started": "2020-10-28T11:26:54.995050Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 使用loc参数调整图例的位置\n",
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "\n",
    "ax.scatter(x, [5]*5, label='散点图')\n",
    "\n",
    "ax.plot(x, [4]*5, label='折线图')\n",
    "\n",
    "ax.bar(x-0.25, [3]*5, width=0.5, label='柱状图1')\n",
    "ax.bar(x+0.25, [3]*5, width=0.5, label='柱状图2')\n",
    "\n",
    "ax.legend(loc=(1, 1));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **ncol**\n",
    "\n",
    "　　利用`ncol`参数我们可以设置图例中个体元素显示的列数，譬如我们前面的例子里有4个要显示的图层，就可以设置为显示4列或2列："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-28T11:26:55.126687Z",
     "iopub.status.busy": "2020-10-28T11:26:55.126687Z",
     "iopub.status.idle": "2020-10-28T11:26:55.267306Z",
     "shell.execute_reply": "2020-10-28T11:26:55.267306Z",
     "shell.execute_reply.started": "2020-10-28T11:26:55.126687Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "\n",
    "ax.scatter(x, [5]*5, label='散点图')\n",
    "\n",
    "ax.plot(x, [4]*5, label='折线图')\n",
    "\n",
    "ax.bar(x-0.25, [3]*5, width=0.5, label='柱状图1')\n",
    "ax.bar(x+0.25, [3]*5, width=0.5, label='柱状图2')\n",
    "\n",
    "ax.legend(ncol=4);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-28T11:26:55.268263Z",
     "iopub.status.busy": "2020-10-28T11:26:55.268263Z",
     "iopub.status.idle": "2020-10-28T11:26:55.425865Z",
     "shell.execute_reply": "2020-10-28T11:26:55.425865Z",
     "shell.execute_reply.started": "2020-10-28T11:26:55.268263Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "\n",
    "ax.scatter(x, [5]*5, label='散点图')\n",
    "\n",
    "ax.plot(x, [4]*5, label='折线图')\n",
    "\n",
    "ax.bar(x-0.25, [3]*5, width=0.5, label='柱状图1')\n",
    "ax.bar(x+0.25, [3]*5, width=0.5, label='柱状图2')\n",
    "\n",
    "ax.legend(ncol=2);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-28T11:26:55.428853Z",
     "iopub.status.busy": "2020-10-28T11:26:55.427826Z",
     "iopub.status.idle": "2020-10-28T11:26:55.581437Z",
     "shell.execute_reply": "2020-10-28T11:26:55.581437Z",
     "shell.execute_reply.started": "2020-10-28T11:26:55.428853Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 使用fontsize控制图例中的文字大小，\n",
    "# 也会顺带着撑大对应多边形图标区域以及整个图例的大小\n",
    "# 使用labelcolor控制文字色彩\n",
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "\n",
    "ax.scatter(x, [5]*5, label='散点图')\n",
    "\n",
    "ax.plot(x, [4]*5, label='折线图')\n",
    "\n",
    "ax.bar(x-0.25, [3]*5, width=0.5, label='柱状图1')\n",
    "ax.bar(x+0.25, [3]*5, width=0.5, label='柱状图2')\n",
    "\n",
    "ax.legend(fontsize=16, labelcolor='red');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-28T11:26:55.582406Z",
     "iopub.status.busy": "2020-10-28T11:26:55.582406Z",
     "iopub.status.idle": "2020-10-28T11:26:55.720035Z",
     "shell.execute_reply": "2020-10-28T11:26:55.720035Z",
     "shell.execute_reply.started": "2020-10-28T11:26:55.582406Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWkAAAFjCAYAAAD/4PooAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAV8klEQVR4nO3df2zV9b3H8dcbWn5PWgZFaID2xo2BKcjoUHTRAmUOg5uSzRKGQBRhg4BjmWEG0aop3EW3ZJs/CY46xtWK87KrU5B6RUFQbOWXUJjucgXKdRZXERgKbT/3j/6QItBOzrfn7TnPR2LS9hzPeX+iPvn6Pd/vpxZCEADAp3bxHgAAcHZEGgAcI9IA4BiRBgDHiDQAOEakAcCxlFi+WM+ePUNWVlYsXxIAEl55efmhEEKvMz0W00hnZWWprKwsli8JAAnPzN4722Oc7gAAx4g0ADhGpAHAMSINAI4RaQBwjEgDgGNEGgAcI9IA4FhMb2YBAG9GjBihzZs3q6qqSq+99pr279+vt99+W5L06KOPqqqqSjNnztQzzzwjSZoyZYpmz56tvXv3auLEiZKkuro69ejRQ7m5uZKkEILMrOk9tmzZogMHDqhz584xn59IA0hoXbt2lSS9+eabevrpp/XTn/5UP/zhD9W7d29J0kMPPaR58+Zp7969ys7OVmpqqtLT07Vu3TpVVlYqMzNT7dq105AhQ1RaWqr9+/dr2rRpKi0tbXqP/Px8dejQIZL5zxlpM0uR9D8Nf0nSnBDCjkgmiYNVWyp135o9OvjRcfVN66zbrh6o64ZlxnusSLFm1tzW7n52p3Yd/Dimrzm47wW669qLm74/03p7f7JPv/zlL7Vr1y6NHz9ekydP1kUXXdR0NCxJFRUV+vDDD9WxY0dNmzZNCxcu1DvvvKPbb79daWlpuvfee/XII49Iktq1a6cQgmbNmqXjx48rLy9Pf/vb3/TXv/5VktS+ffuYrrFRS0fSQyQ9EUKYH8m7x9GqLZW6/ZkdOn6yVpJU+dFx3f5M/Z8/ifofMGtmzYm45rOtd/GEHK1atUr5+fkqKSnRpk2btHz5cm3YsEEffPCBunfvrpSUFB05ckQrV67UunXrtHTpUl144YWaO3eurrjiCm3btk379u1T//79JUmPP/64vvrVr6pTp07KyMhQYWFhJKc4TtVSpC+TdL2ZXSHpPUlTQwg1kU7URu5bs6fpH2qj4ydrdd+aPQn5L7LEmhux5rZ16hFvFM623n9/dqtSD27V7t27NW3aNE2dOlUzZszQ/Pnzdemll2r9+vWSpLfeektr167VRRddpKysLG3evFkLFixQ37591a9fP910001Nrztu3DiZmUpKSvTuu+9q06ZN6t69e6Tra+nqjjclXRVC+LakjyRdc/oTzGyGmZWZWVlVVVUEI0bj4EfH/6WfJwLW3PLPE0Gyrfls63pv9w698cYb6t+/v1auXKkPP/xQGRkZzZ6zZ88e3XDDDfrkk09UUlKigQMH6o477tCwYcN07bXX6siRI/rGN77R9Pw1a9aoS5cuGjlypLKzs5WRkaGdO3dGur6WIr09hPB/DV/vlvS1058QQlgSQsgNIeT26nXG7VBd6pt25v9FOdvPEwFrbvnniSDZ1ny2df3bkBG655571KVLF0nShg0bNGjQIEn1V2tIUnp6uu6//35df/31GjlypPLz8zV69Gg9//zzWr58uX71q181e80pU6YoMzNTR48eVU5OjgoKCjR58uQIV9dypJeb2VAzay/peknbIp2mDd129UB1Tm1+or9zanvddvXAOE0UPdZcjzUnlnOt98iRIzp27JiOHDmi8vJyfetb35Ik1dTUn7V96623tGLFChUWFqqoqEiHDh3SrFmzlJWVpa9//et655139PTTT0uSamvrT6lcfvnlKigo0J49e1RZWdn0no2vGWstnZO+R9J/SDJJ/xVCKG3h+V8ajefmvHwC3hZYM2tOROda7/e+9z0VFBSopKREt956a9MVGJ9++qkkafjw4Ro0aJAGDBig/fv3a/To0Vq0aJHGjx+vu+++W2PGjNGf/vQn1dXVafv27crLy2v23jNnzpQkbdu2TSdPnlRKSuyvarYQQsxeLDc3N/CbWQB8WZ1+k8onn3yiTp06SZIOHz4c2YeEZlYeQsg902PcFg4ADU4NtKSmQEuK/CqOsyHSAOAYkQYAx4g0ADjGBksAEtaTTz6pw4cP68CBA8rLy9OYMWN08803a86cObrkkks+9/zJkydr7ty5GjFiRLOfswsegKQWxYZQmzZt0gMPPKDa2lodO3ZMr732mnJycrRx40YtXbpUIQSFENSu3WcnFFJSUjRnzhx95StfkSSdOHFCr776qt9d8AAgalFtCDVkyBDNnj276Uh65MiRKisr04kTJzRq1Cjt379f9913nyZMmNDs73vwwQeb7ZTXyOsueAAQqag2hHrjjTf0/vvvy8zUs2dPVVRUaNmyZRo/frwmTZqkV155RRMmTNCBAweUk5OjoUOHSpJ+/vOfS5J2796t1atXNzst4nEXPACIVFQbQjXePVhTU6O6ujqlpKToN7/5jYqLi/Xuu++qX79+kqQOHTpo+PDhzU5fSNK0adPUsWPHZj+Lxy54RBpAXPVN66zKMwQ5FhtCderUSfn5+aqpqVFpaany8/P13HPPqbi4WH/4wx8k1Z93Li8v/9wt37t379a8efOa/ezUXfAqKyvbZBc8Ig0grm67emCzc9JSbDeEWrVqlerq6pp2wxsxYoSeffZZpaenS5I2b96sLVu26J///KcGDx6siooK9enTRykpKaqoqGj2WlOmTNHGjRtVVlbWtAtet27dVFxcHJNZz4TrpAHE1XXDMrV4Qo4y0zrLJGWmddbiCTkx2RAqhKB169bp+eeflyRVVVXpgQce0I9//GNNnz5dR48e1cKFC9WjRw8VFhaqrKxMW7duVVFRkWprazV9+nRVV1dLit8ueGywBCBh5efnq7S0VLfeeqtSU1NVUVGhRYsWaejQoVqwYIHS0tLUtWtXzZo1S6+88opOnjyp0aNH695779Vdd92lP/7xjxowYICuuOIKpaena9iwYWd8n23btungwYNf+EPEc22wRKQBJI26urpm10X/K9gFDwAi9kUDLbELHgDgDIg0ADhGpAEkjcYrNL5MuE4aQPQKY3w+t/Bwq542ZMgQbd++ven7SZMm6bbbbmu2N8cTTzyhffv2af78+aqurtbMmTP1yCOPKC0tTe3atYvrDngSkQaQgOrq6iRJXbp0UW1trd5++23t2rVLQ4cO1ebNm7Vnzx5lZWUpIyNDqampTZsjpaam6uDBg9q4caNWrlypZcuW/Us74P3973/XD37wA61fvz5ma+F0B4CEs3r1ao0dO1bbt2/XuHHjdOjQIaWkpGjr1q2qqqpSamqqMjMzNX36dH366acKITT9tu+UlBSNHz9ec+fOVeMlymfaAa9fv346frz+dvb27dururpaU6dO1bFjx2K6Fo6kASSca665Rp07d9aNN96oRYsWNZ2q2Llzp6666qqmfTqKi4tVXl6u6upqjRo1SiEEbdmyRXl5eQohqLCwUKNGjZLU8g547du3V0lJib7//e/HdC1EGkBCWrZsmbp166ZbbrlFjz76qBYuXKi9e/c2bZK0YsUKZWdn684779TQoUO1YcMGbd26VZdeeqleeOGFz51jbmkHvAsuuCCSdXC6A0DCee6555SSkqK0tDQVFRVpwIABWrNmjSZNmqTFixfrpZdeUkZGhubMmdN0ykKS1q5dq5EjR+rFF1/83GueugNednZ2m+yAJxFpAAmopqZGCxculFR/6qN3796fe87LL7+sPn36aOLEiZLqtyx96qmntGzZMv32t7/93OV6U6ZMUWZmpo4ePdq0A97kyZMjXwunOwBEr5WXzMXKddddJ+mz66Jra2ub/XqrEELTL6Z98sknVVdXp3vuuUeTJk1Sdna2xowZo1tuuUUPP/ywOnbs2GwHvE6dOqm4uFiVlZUaOLB+O9WamhqlpESTUzZYApCwBg8erF27duk73/lOs3076urqNG7cOM2bN0/Lly/Xn//8Z4UQtHLlyqYrOebPn6+pU6dq0KBBke6AJ7ELHgCclyh3wJPYBQ8Azku8dsCTiDQAuEakAcAxIg0gabALHgCcQc7jOTF9vR1Td7TqeeyCBwAOxWMXvMOHD2vixImqqalRt27dVFJS0rQ73vngdAeAhBOPXfBWrFihn/3sZ1q7dq0uvPBCrV69OiZr4UgaQMKJxy54s2bNavq6qqpKGRkZMVkLkQaQkNp6F7xGmzZtUnV1tS677LKYrIPTHQASTrx2wfvHP/6hOXPm6Pe//33M1kKkASSceOyCd+LECd1www1avHixBgwYELO1cLoDQORae8lcrMRjF7zHHntM5eXlKioqUlFRkX7yk5+ooKDgvNfCBksAEha74J2GSANIROyCBwCOsQseAOCMiDQAOEakAcAxIg0AjhFpAHCMSAOAY0QaABwj0gDgGJEGAMeINAA4RqQBwLFWRdrMepvZlqiHAQA019oj6fslRfP7ygEAZ9Xipv9mNlrSMUnvRz9O27v72Z3adfDjeI8BIAYG971Ad117cbzHiKlzHkmbWQdJd0r6xTmeM8PMysysrKqqKtbzAUBSO+em/2Z2p6SKEMJKM1sXQsg714ux6T8A/OvOZ9P/fEmzzWydpEvMbGmshwMAnN05z0mHEK5s/LrhSHp69CMBABq1+jrplk51AABij5tZAMAxIg0AjhFpAHCMSAOAY0QaABwj0gDgGJEGAMeINAA4RqQBwDEiDQCOEWkAcIxIA4BjRBoAHCPSAOAYkQYAx4g0ADhGpAHAMSINAI4RaQBwjEgDgGNEGgAcI9IA4BiRBgDHiDQAOEakAcAxIg0AjhFpAHCMSAOAY0QaABwj0gDgGJEGAMeINAA4RqQBwDEiDQCOEWkAcIxIA4BjRBoAHCPSAOAYkQYAx4g0ADhGpAHAMSINAI4RaQBwjEgDgGNEGgAcI9IA4BiRBgDHiDQAOEakAcAxIg0AjhFpAHCMSAOAY0QaABxrVaTNrIeZjTWznlEPBAD4TIuRNrM+kv4iaYSkl82sV+RTAQAkSSmteM7FkuaFEF43s3RJ35S0JtqxAABSK46kQwilDYG+UvVH05uiHwsAILXuSFpmZpIKJJ2UVHvaYzMkzZCk/v37n980hd3P7+//gnKyz3PuL2jH3n1xeV+JNbcl1tx24rVeSdoxdUckr9uqDw5DvdmSNkoaf9pjS0IIuSGE3F69OF0NALHUmg8O55vZlIZv0yR9FOVAAIDPtOZIeomkG83sVUntJb0Y7UgAgEYtnpMOIVRLGtsGswAATsMdhwDgGJEGAMeINAA4RqQBwDEiDQCOEWkAcIxIA4BjRBoAHCPSAOAYkQYAx4g0ADhGpAHAMSINAI4RaQBwjEgDgGNEGgAcI9IA4BiRBgDHiDQAOEakAcAxIg0AjhFpAHCMSAOAY0QaABwj0gDgGJEGAMeINAA4RqQBwDEiDQCOEWkAcIxIA4BjRBoAHCPSAOAYkQYAx4g0ADhGpAHAMSINAI4RaQBwjEgDgGNEGgAcI9IA4BiRBgDHiDQAOEakAcAxIg0AjhFpAHCMSAOAY0QaABwj0gDgGJEGAMeINAA4RqQBwDEiDQCOEWkAcIxIA4BjKS09wcy6S3qy4blHJRWEEE5EPRgAoHVH0j+S9OsQwlhJ70v6brQjAQAatXgkHUJ46JRve0n6ILpxAACnavU5aTMbKSk9hPD6aT+fYWZlZlZWVVUV8wEBIJm1KtJm1kPS7yTddPpjIYQlIYTcEEJur169Yj0fACS1FiNtZh0kPSXp9hDCe9GPBABo1Joj6ZslDZe0wMzWmVlBxDMBABq05oPDhyU93AazAABOw80sAOAYkQYAx4g0ADhGpAHAMSINAI4RaQBwjEgDgGNEGgAcI9IA4BiRBgDHiDQAOEakAcAxIg0AjhFpAHCMSAOAY0QaABwj0gDgGJEGAMeINAA4RqQBwDEiDQCOEWkAcIxIA4BjRBoAHCPSAOAYkQYAx4g0ADhGpAHAMSINAI4RaQBwjEgDgGNEGgAcI9IA4BiRBgDHiDQAOEakAcAxIg0AjhFpAHCMSAOAY0QaABwj0gDgGJEGAMeINAA4RqQBwDEiDQCOEWkAcIxIA4BjRBoAHCPSAOAYkQYAx4g0ADhGpAHAMSINAI4RaQBwrFWRNrPeZrY+6mEAAM21GGkzS5f0uKSu0Y8DADhVa46kayUVSPo44lkAAKdpMdIhhI9DCIfP9riZzTCzMjMrq6qqiu10AJDkzvuDwxDCkhBCbgght1evXrGYCQDQgKs7AMAxIg0AjrU60iGEvAjnAACcAUfSAOAYkQYAx4g0ADhGpAHAMSINAI4RaQBwjEgDgGNEGgAcI9IA4BiRBgDHiDQAOEakAcAxIg0AjhFpAHCMSAOAY0QaABwj0gDgGJEGAMeINAA4RqQBwDEiDQCOEWkAcIxIA4BjRBoAHCPSAOAYkQYAx4g0ADhGpAHAMSINAI4RaQBwjEgDgGNEGgAcI9IA4BiRBgDHiDQAOEakAcAxIg0AjhFpAHCMSAOAY0QaABwj0gDgGJEGAMeINAA4RqQBwDEiDQCOEWkAcIxIA4BjRBoAHCPSAOAYkQYAx4g0ADhGpAHAMSINAI4RaQBwrFWRNrPHzGyjmd0R9UAAgM+0GGkzmyCpfQjhckl9zexr0Y8FAJBadySdJ+mphq//W9K3I5sGANBMSiue01VSZcPXH0u66NQHzWyGpBkN3x41sz2xG6+tvN1T0qG2fldr6zdshjW3FdbcluKzXkmyaee16gFne6A1kT4qqXPD19102tF3CGGJpCVfeDQHzKwshJAb7znaEmtODsm25kRcb2tOd5Trs1McQyX9b2TTAACaac2R9CpJ682sr6Rxki6LdCIAQJMWj6RDCB+r/sPD1yWNCiEcjnqoOPhSn675glhzcki2NSfcei2EEO8ZAABnwR2HQAIysx5mNtbMesZ7FpwfIp2EzKy3ma2P9xxtxcy6m9kLZrbWzP7TzDrEe6YomVkfSX+RNELSy2bWK84jtZmGf7e3xHuOWEr6SCfbLe9mli7pcdVf/54sfiTp1yGEsZLel/TdOM8TtYslzQshFElaI+mbcZ6nLd2vzy4ZTghJHekkveW9VlKB6m9MSgohhIdCCGsbvu0l6YN4zhO1EEJpCOF1M7tS9UfTm+I9U1sws9GSjqn+D+KEkdSRVhLe8h5C+DhBr9BpkZmNlJQeQng93rNEzcxM9X8Yn1T9H8wJreEU1p2SfhHvWWIt2SN9+i3vveM4CyJkZj0k/U7STfGepS2EerMlbZQ0Pt7ztIFfSHowhPBRvAeJtWSP9DlveUdiaDjKekrS7SGE9+I9T9TMbL6ZTWn4Nk3SR/Gbps3kS5ptZuskXWJmS+M8T8wke5S45T053CxpuKQFZrbOzAriPVDElki60cxeldRe0otxnidyIYQrQwh5IYQ8SVtDCNPjPVOsJPXNLGZ2gaT1kl5Swy3vyXq+FoBPSR1pqemStLGSXg0hJNSnwgC+/JI+0gDgWbKfkwYA14g0ADhGpAHAMSINAI4RaQBw7P8BYfM0B/b7dAgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 使用frameon控制是否添加图例底层的矩形托盘，默认为True\n",
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "\n",
    "ax.scatter(x, [5]*5, label='散点图')\n",
    "\n",
    "ax.plot(x, [4]*5, label='折线图')\n",
    "\n",
    "ax.bar(x-0.25, [3]*5, width=0.5, label='柱状图1')\n",
    "ax.bar(x+0.25, [3]*5, width=0.5, label='柱状图2')\n",
    "\n",
    "ax.legend(frameon=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-28T11:26:55.721017Z",
     "iopub.status.busy": "2020-10-28T11:26:55.721017Z",
     "iopub.status.idle": "2020-10-28T11:26:55.877588Z",
     "shell.execute_reply": "2020-10-28T11:26:55.877588Z",
     "shell.execute_reply.started": "2020-10-28T11:26:55.721017Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 使用fancybox控制底层托盘是否为圆角矩形，默认为True\n",
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "\n",
    "ax.scatter(x, [5]*5, label='散点图')\n",
    "\n",
    "ax.plot(x, [4]*5, label='折线图')\n",
    "\n",
    "ax.bar(x-0.25, [3]*5, width=0.5, label='柱状图1')\n",
    "ax.bar(x+0.25, [3]*5, width=0.5, label='柱状图2')\n",
    "\n",
    "ax.legend(fancybox=False); # 不绘制圆角矩形托盘"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-28T11:26:55.878631Z",
     "iopub.status.busy": "2020-10-28T11:26:55.878631Z",
     "iopub.status.idle": "2020-10-28T11:26:56.032178Z",
     "shell.execute_reply": "2020-10-28T11:26:56.032178Z",
     "shell.execute_reply.started": "2020-10-28T11:26:55.878631Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 使用shadow控制是否为底层托盘添加阴影效果，默认为False\n",
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "\n",
    "ax.scatter(x, [5]*5, label='散点图')\n",
    "\n",
    "ax.plot(x, [4]*5, label='折线图')\n",
    "\n",
    "ax.bar(x-0.25, [3]*5, width=0.5, label='柱状图1')\n",
    "ax.bar(x+0.25, [3]*5, width=0.5, label='柱状图2')\n",
    "\n",
    "ax.legend(shadow=True); # 添加托盘阴影"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-28T11:26:56.033159Z",
     "iopub.status.busy": "2020-10-28T11:26:56.033159Z",
     "iopub.status.idle": "2020-10-28T11:26:56.177762Z",
     "shell.execute_reply": "2020-10-28T11:26:56.177762Z",
     "shell.execute_reply.started": "2020-10-28T11:26:56.033159Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 使用framealpha控制底层托盘的透明度，默认为0.8\n",
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "\n",
    "ax.scatter(x, [5]*5, label='散点图')\n",
    "\n",
    "ax.plot(x, [4]*5, label='折线图')\n",
    "\n",
    "ax.bar(x-0.25, [3]*5, width=0.5, label='柱状图1')\n",
    "ax.bar(x+0.25, [3]*5, width=0.5, label='柱状图2')\n",
    "\n",
    "ax.legend(framealpha=1); # 设置托盘完全不透明"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-28T11:26:56.178802Z",
     "iopub.status.busy": "2020-10-28T11:26:56.178802Z",
     "iopub.status.idle": "2020-10-28T11:26:56.316405Z",
     "shell.execute_reply": "2020-10-28T11:26:56.316405Z",
     "shell.execute_reply.started": "2020-10-28T11:26:56.178802Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TPUVS4HQHADhGpAHAMSINAI4RaQBwjEgDgGNEGgAcI9IA4BiRBgDHyhVpM2toZgviPQwAoLTyHkmPl1Q9noMAAL6vzLeFm1kXSVskrYv/OBXvjy9/ok/X5CV6DAAxcOSBtXVLr1aJHiOmfvRI2syqSBoj6Qd/YZeZDTKzLDPLysnJifV8AFCp/eim/2Y2RtLiEMJUM5sVQuj8Y0/Gpv8AsO/2Z9P/rpKuNrNZko4xs0djPRwA4If96DnpEEKnnR8XH0kPjP9IAICdyn2ddFmnOgAAscebWQDAMSINAI4RaQBwjEgDgGNEGgAcI9IA4BiRBgDHiDQAOEakAcAxIg0AjhFpAHCMSAOAY0QaABwj0gDgGJEGAMeINAA4RqQBwDEiDQCOEWkAcIxIA4BjRBoAHCPSAOAYkQYAx4g0ADhGpAHAMSINAI4RaQBwjEgDgGNEGgAcI9IA4BiRBgDHiDQAOEakAcAxIg0AjhFpAHCMSAOAY0QaABwj0gDgGJEGAMeINAA4RqQBwDEiDQCOEWkAcIxIA4BjRBoAHCPSAOAYkQYAx4g0ADhGpAHAMSINAI4RaQBwjEgDgGNEGgAcK1ekzayemXUzs/rxHggAsEuZkTazxpKmSzpB0ptmlhn3qQAAkqS0cjymlaTrQghzzayupOMkvRbfsQAAUjmOpEMIbxQHupOiR9P/jf9YAACpfEfSMjOT1FdSgaSiPe4bJGmQJDVt2nT/pvlDxv59/U/U+pD9nPsnWrTyi4R8X4k1VyTWXHEStV5JWnTxorg8b7leOAxRV0t6V1LPPe6bHEJoG0Jom5nJ6WoAiKXyvHB4g5n1L/60jqTceA4EANilPEfSkyX1M7PZklIl/Se+IwEAdirznHQI4TtJ3SpgFgDAHnjHIQA4RqQBwDEiDQCOEWkAcIxIA4BjRBoAHCPSAOAYkQYAx4g0ADhGpAHAMSINAI4RaQBwjEgDgGNEGgAcI9IA4BiRBgDHiDQAOEakAcAxIg0AjhFpAHCMSAOAY0QaABwj0gDgGJEGAMeINAA4RqQBwDEiDQCOEWkAcIxIA4BjRBoAHCPSAOAYkQYAx4g0ADhGpAHAMSINAI4RaQBwjEgDgGNEGgAcI9IA4BiRBgDHiDQAOEakAcAxIg0AjhFpAHCMSAOAY0QaABwj0gDgGJEGAMeINAA4RqQBwDEiDQCOEWkAcIxIA4BjRBoAHCPSAOBYWlkPMLMMSc8UP3azpL4hhPx4DwYAKN+R9EWSJoQQuklaJ6l7fEcCAOxU5pF0CGHSbp9mSvomfuMAAHZX7nPSZtZBUt0Qwtw9bh9kZllmlpWTkxPzAQGgMitXpM2snqSJki7b874QwuQQQtsQQtvMzMxYzwcAlVqZkTazKpKek3RjCGFV/EcCAOxUniPpAZKOlzTazGaZWd84zwQAKFaeFw7/LOnPFTALAGAPvJkFABwj0gDgGJEGAMeINAA4RqQBwDEiDQCOEWkAcIxIA4BjRBoAHCPSAOAYkQYAx4g0ADhGpAHAMSINAI4RaQBwjEgDgGNEGgAcI9IA4BiRBgDHiDQAOEakAcAxIg0AjhFpAHCMSAOAY0QaABwj0gDgGJEGAMeINAA4RqQBwDEiDQCOEWkAcIxIA4BjRBoAHCPSAOAYkQYAx4g0ADhGpAHAMSINAI4RaQBwjEgDgGNEGgAcI9IA4BiRBgDHiDQAOEakAcAxIg0AjhFpAHCMSAOAY0QaABwj0gDgGJEGAMeINAA4RqQBwDEiDQCOlSvSZtbQzObEexgAQGllRtrM6kp6TFLN+I8DANhdeY6kiyT1lZQX51kAAHsoM9IhhLwQwsYfut/MBplZlpll5eTkxHY6AKjk9vuFwxDC5BBC2xBC28zMzFjMBAAoxtUdAOAYkQYAx8od6RBC5zjOAQDYC46kAcAxIg0AjhFpAHCMSAOAY0QaABwj0gDgGJEGAMeINAA4RqQBwDEiDQCOEWkAcIxIA4BjRBoAHCPSAOAYkQYAx4g0ADhGpAHAMSINAI4RaQBwjEgDgGNEGgAcI9IA4BiRBgDHiDQAOEakAcAxIg0AjhFpAHCMSAOAY0QaABwj0gDgGJEGAMeINAA4RqQBwDEiDQCOEWkAcIxIA4BjRBoAHCPSAOAYkQYAx4g0ADhGpAHAMSINAI4RaQBwjEgDgGNEGgAcI9IA4BiRBgDHiDQAOEakAcAxIg0AjhFpAHCMSAOAY0QaABwj0gDgWLkibWZ/NbN3zeymeA8EANilzEib2TmSUkMIJ0o60Myax38sAIBUviPpzpKeK/54pqSOcZsGAFBKWjkeU1PS6uKP8yT9Yvc7zWyQpEHFn242s+zYjVdRPq4vaX1Ff1er6G9YCmuuKKy5IiVmvZJkl+zXqg/+oTvKE+nNkqoXf3yA9jj6DiFMljT5J4/mgJllhRDaJnqOisSaK4fKtuZkXG95TnfM165THEdL+jxu0wAASinPkfSLkuaY2YGSTpfUPq4TAQBKlHkkHULIU/TFw7mSTg0hbIz3UAnwP3265idizZVDZVtz0q3XQgiJngEA8AN4xyGQhMysnpl1M7P6iZ4F+4dIV0Jm1tDM5iR6jopiZhlm9oqZvW5mL5hZlUTPFE9m1ljSdEknSHrTzDITPFKFKf67vSDRc8RSpY90ZXvLu5nVlfSYote/VxYXSZoQQugmaZ2k7gmeJ95aSbouhHCbpNckHZfgeSrSeO26ZDgpVOpIV9K3vBdJ6qvoG5MqhRDCpBDC68WfZkr6JpHzxFsI4Y0Qwlwz66To0fR/Ez1TRTCzLpK2KPqDOGlU6kirEr7lPYSQl6RX6JTJzDpIqhtCmJvoWeLNzEzRH8YFiv5gTmrFp7DGSBqV6FlirbJHes+3vDdM4CyIIzOrJ2mipMsSPUtFCFFXS3pXUs9Ez1MBRkl6KISQm+hBYq2yR/pH3/KO5FB8lPWcpBtDCKsSPU+8mdkNZta/+NM6knITN02F6SrpajObJekYM3s0wfPETGWPEm95rxwGSDpe0mgzm2VmfRM9UJxNltTPzGZLSpX0nwTPE3chhE4hhM4hhM6SFoYQBiZ6plip1G9mMbPakuZImqHit7xX1vO1AHyq1JGWSi5J6yZpdgghqV4VBvC/r9JHGgA8q+znpAHANSINAI4RaQBwjEgDgGNEGgAc+3/OUH05vzG1zAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 使用facecolor和edgecolor分别设置托盘的填充色与轮廓色\n",
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "\n",
    "ax.scatter(x, [5]*5, label='散点图')\n",
    "\n",
    "ax.plot(x, [4]*5, label='折线图')\n",
    "\n",
    "ax.bar(x-0.25, [3]*5, width=0.5, label='柱状图1')\n",
    "ax.bar(x+0.25, [3]*5, width=0.5, label='柱状图2')\n",
    "\n",
    "ax.legend(facecolor='#eddd9e', edgecolor='red');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-28T11:26:56.317379Z",
     "iopub.status.busy": "2020-10-28T11:26:56.317379Z",
     "iopub.status.idle": "2020-10-28T11:26:56.474945Z",
     "shell.execute_reply": "2020-10-28T11:26:56.474945Z",
     "shell.execute_reply.started": "2020-10-28T11:26:56.317379Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 使用title设置图例的标题，使用title_fontsize设置图例标题的字体大小\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "\n",
    "ax.scatter(x, [5]*5, label='散点图')\n",
    "\n",
    "ax.plot(x, [4]*5, label='折线图')\n",
    "\n",
    "ax.bar(x-0.25, [3]*5, width=0.5, label='柱状图1')\n",
    "ax.bar(x+0.25, [3]*5, width=0.5, label='柱状图2')\n",
    "\n",
    "ax.legend(title='标题', title_fontsize=20, loc='upper right');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-28T11:26:56.475942Z",
     "iopub.status.busy": "2020-10-28T11:26:56.475942Z",
     "iopub.status.idle": "2020-10-28T11:26:56.609612Z",
     "shell.execute_reply": "2020-10-28T11:26:56.609612Z",
     "shell.execute_reply.started": "2020-10-28T11:26:56.475942Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 使用borderpad设置图例元素到托盘边缘的填充距离\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "\n",
    "ax.scatter(x, [5]*5, label='散点图')\n",
    "\n",
    "ax.plot(x, [4]*5, label='折线图')\n",
    "\n",
    "ax.bar(x-0.25, [3]*5, width=0.5, label='柱状图1')\n",
    "ax.bar(x+0.25, [3]*5, width=0.5, label='柱状图2')\n",
    "\n",
    "ax.legend(title='标题', \n",
    "          title_fontsize=20, \n",
    "          loc='upper right',\n",
    "          borderpad=3);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-28T11:26:56.610571Z",
     "iopub.status.busy": "2020-10-28T11:26:56.610571Z",
     "iopub.status.idle": "2020-10-28T11:26:56.753216Z",
     "shell.execute_reply": "2020-10-28T11:26:56.753216Z",
     "shell.execute_reply.started": "2020-10-28T11:26:56.610571Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWkAAAFjCAYAAAD/4PooAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAX70lEQVR4nO3de3DV9ZnH8c8TEgiIknBV5JLssKXYBWTNqKltDRDaYtGuHQYcVg2jCC0MdmirKUUQdaR1ajvTekNXC5ZSi1SXtraCZNeISJQmIohcCi2LJFSNGLkJ5vbdP5IcSbjkAOfkPOS8XzPO5Fxy8nzH4zs/fuf3RQshCADgU0qiBwAAnByRBgDHiDQAOEakAcAxIg0AjhFpAHAsNZYv1rNnz5CVlRXLlwSAdq+srOzDEEKvEz0W00hnZWWptLQ0li8JAO2eme0+2WOc7gAAx4g0ADhGpAHAsZiek04GNTU1Ki8v19GjRxM9CoBzTHp6uvr166e0tLSov4dIn6by8nKdf/75ysrKkpklehwA54gQgvbt26fy8nJlZ2dH/X2c7jhNR48eVY8ePQg0gNNiZurRo8dp/ymcSJ8BAg2cm+rq6hL688+kHUQaQLs1bNiwZrcnTZp03F6OZ555Rg888IAkqaqqShMmTNBHH32k+vp6SVJ9fb0yMjKUn5+v/Px8jR49OvJ1fn6+evTooSNHjsRtDZyTxnEuv/xyrV+/XpWVlXrttde0Z88ebd68WZL0+OOPq7KyUtOmTdPzzz8vSbr55ps1Y8YM7dq1SzfccIOkhjd29+7dlZOTI6nhfNyxRxEbNmxQeXm5Onfu3MarwynN75boCWKifl6VJKlLly6qq6vT5s2btWXLFg0fPlzr16/X9u3blZWVpd69eystLU0dOnSQJKWlpWnv3r1at26dli9frkWLFiklJUXDhg1TUVGR9uzZo8mTJ6uoqCjys/Lz89WxY0dJ0vvvv6/x48fr1VdfjdlaOJLGcc477zxJ0l//+lf9/ve/V25uru655x4tXLhQkvToo49q1qxZ2rVrl6SGN3ZmZqZ2796tiooKSWr2xl60aJEkqaioKPLPiBEjIm9sINZWrlypMWPGaNOmTRo7dqw+/PBDpaam6q233lJlZaXS0tJ08cUXa8qUKfr0008VQlBNTY1SU1OVmpqqcePG6fbbb1fT/7kqJSVFIQRNnz5dR44cUV5envr37x85gu7QoYOqqqpUUFCgw4cPx3QtpzySNrNUSf9o/EeSZoYQ3o7pBAm0YkOFfrpqu/Z+fER9Mzrrjq8N1n+MuDjq77/nT+9oy94DcZww9mrrg6pr6yNHth1TU5SaYrqk7wX6as8DeuCBB7RlyxaNGzdON954owYNGhQ5GpakrVu3at++ferUqZMmT56suXPnaseOHZo9e7YyMjJ03333RWJ+ojf23//+d/3tb3+TpMjRS7xVfVKt9/cfVXVdvTp2SFGfbunK7NK+f0Ek45qPlZuXr7s6d9ZNN92kBQsWRN7D77zzjq6++mrl5eVJkhYvXqyysjJVVVVp5MiRCiFow4YNysvLUwhB8+fP18iRIyVJTz/9tHr06KH09HT17t1b8+fPb/YnwQ4dOmjZsmX65je/GdO1tHa6Y5ikZ0IIhTH9qQ6s2FCh2c+/rSM1DR8kVHx8RLOfb/j9czqhPpfU1gd9WlsnNf5vLUNovJ3aEMvc3FytWLFC+fn5WrZsmUpKSrRkyRKtXbtWH3zwgbp166bU1FQdPHhQy5cvV3FxsZ588kldeOGFuv3223XVVVdp48aNevfddzVgwABJrb+x463qk2pVVB1RfeMRUXVdvSqqGo5+2mu0knHNLVVUHdHj//WUunbtqttuu02PP/645s6dq127dmnVqlXq0qWLli5dquzsbM2bN0/Dhw/X2rVr9dZbb+mKK67Qiy++eNz7dOzYsTIzLVu2TDt37lRJSYm6dfvs9NAFF1wQl7W0FukrJV1vZldJ2i2pIIRQG5dJ2thPV22PBLrJkZo6/XTV9qgjffe1X4jHaHGz7Z8HVF1Xf9z9HTuk6PMXXaDDhw+ruLhY27Zt0+TJk1VQUKCpU6eqsLBQV1xxReQ825tvvqnVq1dr0KBBysrK0vr16zVnzhz17dtX/fv31y233BJ57dbe2PH2/v6jkVg1qQ9B7+8/2m6DlYxrbunl1S+qpt6UkZGhefPmaeDAgVq1apXmz5+vvLw85eXlqb6+XjNnzmz2od/q1auVm5url1566bgj4qa45+bmqqKiQr1799Y777wT97W0dk76r5KuDiF8SdLHkq5p+QQzm2pmpWZWWllZGYcR42Pvxyf+NPZk97cHJwr0sfeXlpbqjTfe0IABA7R8+XLt27dPvXv3bvbc7du3a8KECTp69KiWLVumwYMH66677tKIESN07bXX6uDBg/r85z8fef6xb+zs7Ow2e2O3XFu097cHybjmlupq63Tr7T+QJF1zzTXq06fPcc95+eWXddFFF0U+7K6urtazzz6rRYsW6Ze//OVxl+vdfPPNuvjii3Xo0CENHTpUEydO1I033hj3tbQW6U0hhH82fr1N0r+2fEII4YkQQk4IIadXrxP+dagu9c048R+5T3Z/e9Cxw4n/dTfdf/XVV+vee+9Vly5dJElr167VkCFDJClyOVJmZqYefPBBXX/99crNzVV+fr5GjRqlv/zlL1qyZIl+9rOfNXvtRL2xW64t2vvbg2Rcc0ujvv4N/Ut2diS0LYMbQlBeXp5+9KMfqba2VvX19br33ns1adIkZWdna/To0brtttv06aefNvv+L37xi5o4caK2b98e+ZBckmpr43eCobXTHUvM7H5JmyVdL2lB3CZpY3d8bXCzc9KS1Dmtg+742uAEThVffbqlNztXKUkpZurTLT1y++DBgzp8+LAOHjyosrKyyIeATW/CN998U0uXLlVdXZ26d++un/zkJ5ozZ46ysrL0uc99Tjt27NCOHTs0fvz4Zm/s9PR0LV68WBUVFRo8eHDkNVNT43sVaDRrbm/Oas3z98dxsvhoeQ5e+my9TVdajB07VikpDb+kXn/9dS1YsEBjx47VrFmzVFNTo/Xr1yuEoOXLl0uSZs+ercLCQu3cuVNDhgzRpk2bIh82Npk2bZokaePGjZErQySpuLg4puuz0OLcVbMHzf5N0m8lmaQ/hhDmnOrFcnJywrn0l/6fydUdW7dujRxdnota+9T/uuuu06hRo9S1a1elpaWpoKBAknTJJZdoy5Ytqqys1CeffKKBAwdqz549+sY3vqEFCxZo3Lhxuueee/TQQw/pueee05e//GVlZmZqxIgRJ5xj48aN2rt3b5t8iJiMVzok25qjXW9dXd0ZXVW0f//+mH2WcqKGmFlZCCHnRM8/5WFMCGGzGq7waJf+Y8TF7fZKjpPJ7NLxlP+xTpo0Sfv379fu3bsjRw633nqrfvvb30qSjj2l1b9/fw0bNixy3vruu+9WYWGh0tPTI6dHmo4uWm5maTqqaQutrbk9OtM1D316aBymaXtvFzRcqTVs2DBt2rQpcv+kSZN0xx13NLus9JlnntG7776rwsJCVVVVadq0aVq4cKEyMjKUkpKi+vp6DRw4MGEbs9hx6NjZXsd9ukpKSvTwww+rrq5Ohw8f1muvvaahQ4dq3bp1evLJJxVCUAihWWBTU1M1c+ZMnX/++ZIaPnxZs2aNUlJSNHz48Kh2aQGx1nSQ0JY7Dvfv368bbrhBtbW16tq1q5YtWxaT93jyfJJwjmm6jrvi4yMK+uw67hUbKlr93jM1bNgwzZgxQwUFBbruuuv0/e9/X6WlpaqurtbIkSM1aNAgrVix4rjve+SRRyI7CdesWRO5P5pdWkA8JGLH4dKlS/W9731Pq1ev1oUXXqiVK1fGZC0cSTsVi+u4T9cbb7yh9957T2amnj17auvWrVq0aJHGjRunSZMm6ZVXXtG3vvUtlZeXa+jQoRo+fLgk6Qc/aLjUadu2bVq5cqUuvfTSyGsmejMLktM111yjzm2843D69OmRrysrK4+7fPVMEWmnEnEd96hRo7RgwYLIJUmpqan6xS9+ocWLF2vnzp3q37+/JKljx4667LLLmv2RT5ImT56sTp06Nbsv0ZtZkLwWLVrUpjsOm5SUlKiqqkpXXnllTNZBpJ3qm9FZFScIcryv405PT1d+fr5qa2tVVFSk/Px8vfDCC1q8eLF+/etfS2o471xWVnbcJUnbtm3TrFmzmt2XqF1aSG4vvPCCUlNT23zH4UcffaSZM2fqueeei9laOCft1B1fG6zOac3P2bbVddwrVqzQH//4x8jtyy+/XP/4xz+UmZkpSVq/fr02bNigRx99VMXFxXrssce0YsUK7dy5U9XV1c1eK9GbWZCcamtrNXfuXEltt+OwurpaEyZM0I9//GMNHDgwZmvhSNqppvPObXl1h9RweVFxcbEOHTqk8ePHq7KyUg8//LC+/e1va8qUKVq4cKHmzp2rkpIS3Xnnnbrzzju1Y8cO/epXv9Jdd92lKVOmqLi4WJmZmS42s+D0NF261l4cu+Pw2A+qm3Ycjh49Wr/73e9OuuPwscceU6dOnaJ6Lz/11FMqKyvT/fffr/vvv1/f+c53NHHixLNewyk3s5yuc20zy5k41zeztCY/P19FRUX67ne/q7S0NG3dulULFizQ8OHDNWfOHGVkZOi8887T9OnT9corr6impkajRo3Sfffdp7vvvlu/+c1vNHDgQF111VVuNrMgeTVtwvrqV7/a7NLR+vr6yI7DJUuW6A9/+ENkx2HTlRyFhYUqKCjQkCFDYvpePt3NLET6NLX3SLdUX19/xhtPYrlLC0ikRO445Jz0GYjlLzbvzmZnIIFGexGr9/KZtINIn6b09HTt27cvqUIN4OyFELRv3z6lp5/eX+7FpzanqV+/fiovL9e59HdnA/AhPT1d/fr1O63vIdKnKS0tTdnZ2YkeA0CS4HQHADhGpAHAMSINAI4RaQBwjEgDgGNEGgAcI9IA4BiRBgDHiDQAOEakAcAxIg0AjhFpAHCMSAOAY0QaABwj0gDgGJEGAMeINAA4RqQBwDEiDQCOEWkAcIxIA4BjRBoAHCPSAOAYkQYAx4g0ADhGpAHAMSINAI4RaQBwjEgDgGNEGgAcI9IA4BiRBgDHiDQAOEakAcAxIg0AjhFpAHCMSAOAY0QaABwj0gDgGJEGAMeINAA4FlWkzayPmW2I9zAAgOaiPZJ+UFLneA4CADheamtPMLNRkg5Lei/+47S9e/70jrbsPZDoMQDEwCV9L9Dd134h0WPE1CmPpM2so6R5kn54iudMNbNSMyutrKyM9XwAkNQshHDyB83mSdoaQlhuZsUhhLxTvVhOTk4oLS2N8YgA0L6ZWVkIIedEj7V2Tjpf0gwzK5Z0qZk9GevhAAAnd8pz0iGErzR93XgkPSX+IwEAmkR9nXRrpzoAALHHZhYAcIxIA4BjRBoAHCPSAOAYkQYAx4g0ADhGpAHAMSINAI4RaQBwjEgDgGNEGgAcI9IA4BiRBgDHiDQAOEakAcAxIg0AjhFpAHCMSAOAY0QaABwj0gDgGJEGAMeINAA4RqQBwDEiDQCOEWkAcIxIA4BjRBoAHCPSAOAYkQYAx4g0ADhGpAHAMSINAI4RaQBwjEgDgGNEGgAcI9IA4BiRBgDHiDQAOEakAcAxIg0AjhFpAHCMSAOAY0QaABwj0gDgGJEGAMeINAA4RqQBwDEiDQCOEWkAcIxIA4BjRBoAHCPSAOAYkQYAx6KKtJl1N7MxZtYz3gMBAD7TaqTN7CJJf5Z0uaSXzaxX3KcCAEiSUqN4zhckzQohvG5mmZL+XdKq+I4FAJCiOJIOIRQ1BvorajiaLon/WAAAKbojaZmZSZooqUZSXYvHpkqaKkkDBgw4u2nmdzu77z9DQ7PPcu4z9PaudxPycyXW3JZYc9tJ1Hol6e2Ct+PyulF9cBgazJC0TtK4Fo89EULICSHk9OrF6WoAiKVoPjgsNLObG29mSPo4ngMBAD4TzZH0E5JuMrM1kjpIeim+IwEAmrR6TjqEUCVpTBvMAgBogR2HAOAYkQYAx4g0ADhGpAHAMSINAI4RaQBwjEgDgGNEGgAcI9IA4BiRBgDHiDQAOEakAcAxIg0AjhFpAHCMSAOAY0QaABwj0gDgGJEGAMeINAA4RqQBwDEiDQCOEWkAcIxIA4BjRBoAHCPSAOAYkQYAx4g0ADhGpAHAMSINAI4RaQBwjEgDgGNEGgAcI9IA4BiRBgDHiDQAOEakAcAxIg0AjhFpAHCMSAOAY0QaABwj0gDgGJEGAMeINAA4RqQBwDEiDQCOEWkAcIxIA4BjRBoAHCPSAOAYkQYAx4g0ADhGpAHAMSINAI4RaQBwjEgDgGOprT3BzLpJ+l3jcw9JmhhCqI73YACA6I6k/1PSz0MIYyS9J+nr8R0JANCk1SPpEMKjx9zsJemD+I0DADhW1OekzSxXUmYI4fUW9081s1IzK62srIz5gACQzKKKtJl1l/SQpFtaPhZCeCKEkBNCyOnVq1es5wOApNZqpM2so6RnJc0OIeyO/0gAgCbRHEnfKukySXPMrNjMJsZ5JgBAo2g+OHxM0mNtMAsAoAU2swCAY0QaABwj0gDgGJEGAMeINAA4RqQBwDEiDQCOEWkAcIxIA4BjRBoAHCPSAOAYkQYAx4g0ADhGpAHAMSINAI4RaQBwjEgDgGNEGgAcI9IA4BiRBgDHiDQAOEakAcAxIg0AjhFpAHCMSAOAY0QaABwj0gDgGJEGAMeINAA4RqQBwDEiDQCOEWkAcIxIA4BjRBoAHCPSAOAYkQYAx4g0ADhGpAHAMSINAI4RaQBwjEgDgGNEGgAcI9IA4BiRBgDHiDQAOEakAcAxIg0AjhFpAHCMSAOAY0QaABwj0gDgGJEGAMeINAA4RqQBwLGoIm1mfczs1XgPAwBortVIm1mmpKclnRf/cQAAx4rmSLpO0kRJB+I8CwCghVYjHUI4EELYf7LHzWyqmZWaWWllZWVspwOAJHfWHxyGEJ4IIeSEEHJ69eoVi5kAAI24ugMAHCPSAOBY1JEOIeTFcQ4AwAlwJA0AjhFpAHCMSAOAY0QaABwj0gDgGJEGAMeINAA4RqQBwDEiDQCOEWkAcIxIA4BjRBoAHCPSAOAYkQYAx4g0ADhGpAHAMSINAI4RaQBwjEgDgGNEGgAcI9IA4BiRBgDHiDQAOEakAcAxIg0AjhFpAHCMSAOAY0QaABwj0gDgGJEGAMeINAA4RqQBwDEiDQCOEWkAcIxIA4BjRBoAHCPSAOAYkQYAx4g0ADhGpAHAMSINAI4RaQBwjEgDgGNEGgAcI9IA4BiRBgDHiDQAOEakAcAxIg0AjhFpAHCMSAOAY0QaABwj0gDgGJEGAMeiirSZPWVm68zsrngPBAD4TKuRNrNvSeoQQviipL5m9q/xHwsAIEV3JJ0n6dnGr/9X0pfiNg0AoJnUKJ5znqSKxq8PSBp07INmNlXS1Mabh8xse+zGayube0r6sK1/qrX1D2yGNbcV1tyWErNeSbLJZ7XqgSd7IJpIH5LUufHrrmpx9B1CeELSE2c8mgNmVhpCyEn0HG2JNSeHZFtze1xvNKc7yvTZKY7hkv4vbtMAAJqJ5kh6haRXzayvpLGSrozrRACAiFaPpEMIB9Tw4eHrkkaGEPbHe6gEOKdP15wh1pwckm3N7W69FkJI9AwAgJNgxyHQDplZdzMbY2Y9Ez0Lzg6RTkJm1sfMXk30HG3FzLqZ2YtmttrM/tvMOiZ6pngys4sk/VnS5ZJeNrNeCR6pzTS+tzckeo5YSvpIJ9uWdzPLlPS0Gq5/Txb/KennIYQxkt6T9PUEzxNvX5A0K4Rwv6RVkv49wfO0pQf12SXD7UJSRzpJt7zXSZqoho1JSSGE8GgIYXXjzV6SPkjkPPEWQigKIbxuZl9Rw9F0SaJnagtmNkrSYTX8Im43kjrSSsIt7yGEA+30Cp1WmVmupMwQwuuJniXezMzU8Mu4Rg2/mNu1xlNY8yT9MNGzxFqyR7rllvc+CZwFcWRm3SU9JOmWRM/SFkKDGZLWSRqX6HnawA8lPRJC+DjRg8Raskf6lFve0T40HmU9K2l2CGF3oueJNzMrNLObG29mSPo4cdO0mXxJM8ysWNKlZvZkgueJmWSPElvek8Otki6TNMfMis1sYqIHirMnJN1kZmskdZD0UoLnibsQwldCCHkhhDxJb4UQpiR6plhJ6s0sZnaBpFcl/Y8at7wn6/laAD4ldaSlyCVpYyStCSG0q0+FAZz7kj7SAOBZsp+TBgDXiDQAOEakAcAxIg0AjhFpAHDs/wGvo12lxlKO8wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "\n",
    "ax.scatter(x, [5]*5, label='散点图')\n",
    "\n",
    "ax.plot(x, [4]*5, label='折线图')\n",
    "\n",
    "ax.bar(x-0.25, [3]*5, width=0.5, label='柱状图1')\n",
    "ax.bar(x+0.25, [3]*5, width=0.5, label='柱状图2')\n",
    "\n",
    "ax.legend(labelspacing=0, # 控制竖直方向上每个图例元素之间的距离\n",
    "          handlelength=5, # 控制每个图例个体对应图标的宽度\n",
    "          handletextpad=0, # 控制图标与对应文字标签之间的距离\n",
    "          columnspacing=10, # 设置不同列之间的水平间隔\n",
    "          ncol=2);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 课后测验：\n",
    "\n",
    "　　通过今天的学习，我们对`matplotlib`中`legend()`常用参数有了了解，下面请你以`x`和`y`分别作为x轴与y轴，`colors`配合`base_colors`作为色彩映射依据列，譬如`colors`中取值在0、1、2、3之内，取值为i则对应`base_colors`中的第i个色彩，在下面所给代码的基础上，补充相应的代码，实现对下图风格的模仿：\n",
    "\n",
    "<center>\n",
    "    <img src='imgs/课后题目.png' width=500></img>\n",
    "</center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-28T11:26:56.754203Z",
     "iopub.status.busy": "2020-10-28T11:26:56.754203Z",
     "iopub.status.idle": "2020-10-28T11:26:56.757169Z",
     "shell.execute_reply": "2020-10-28T11:26:56.757169Z",
     "shell.execute_reply.started": "2020-10-28T11:26:56.754203Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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t12vXaXWg7p0vZQw2qoyZI+U9Hib9lzp8yoDNd13MEK8nJ7HloWQJUw8llUqFUAA9zS3z/kZRth9PT3MLQu+dL2UcpXWe3eNJTbb5LCyZ3eNpuC14byhvTWttDl8yYPtdFzNEUTiJ6et1hRNfr0NKJS5dsl48KigoCCvTF+NuUwu02rlmJFtmJ612GnebWrAyfbHkN9IcmRza2towDCA2Jdmp68amJGP43vlCxtvNTgB/MmD7XRczRFE4AVdfr+vWrUPY1BSuX/hkzu/PnPnA6vHXL3yCsKkprFu3zrkOiJDHH3/c7t/t7fHYQyx7PI76b45UYkcscUYGbCH1laqzkD0KJzB9vaa68PXaVFePtrY2qx5KaWlpKFy7Fu9VVKASmPXa6enpmXOcudfOQ2vXCt6+zgZXrlyx+3d7ezz2EMsej6P+A6L1yGEMExmwDVfvulghisIJuPx6NW2AlVy+jPL2DixMTcbk0DD6e7qg0+rQ09yCu00tCJuawkMS9q5wFvM9Hmfci6Wyx0M8crhB6itVZyGKwgm4/notLCxERkbGzNdhXT2SpnWoO/n+7NfhNgl8HTrLkSNH7P7dtMdT2tQC7f3rGJkJTHs820Swx2Ov/97ikeNoDHCBq+/6xJgGI8MjaG5ullRAHlEUTuDu12t4eDjUarXdqM60tDSkpaVhl0aD1157DV//+tclNeCc5fLly8jJybF7zLp163Dp1i1cv/CJ3TgKE2La47HVfynHjljCZAywjaN3fWpqEiODg9Dp9PD1lcNgMKK7owOfXfwE041N+GNAAJQ3b0jG/Ecis52kuLgYpW1t2FC0l/HX6we/+Q3iBofhHx3llA2ZFGxhLgOpRmbb6r8r47C8+Ci2qVQoKirioqmcwdd7YE3Gg329aGloQkd3N6ZAwUjRGOvrx8jgXciDA6EfHcPy1FSs+spXZs1/JpMxD+Y/EpnNF85+vZa+W4z26zdBZ+cgidiQOcPaHk9sSjJ8Fb4u7fFwkdOHrWvayw9mC2/KD8YWlu967Y0bqLvdgHFfOQIT4hARFYOhO72YAgVZbCwmOjqAu0MIWhGC8JgYANIx/xFF4SS2PJQs0WqnUfpuMZqqrmHRqlXY9vffd9qG/MQTT3DaFzHgjAws93ia6urnrN6Y7PFw4UHkzjWt9d/bPHL4eg/M3/WTra0YDw6EX0IS0rIyIJP7YmJkBMOjo/CLi4VichLykBD45eSgta8PIRWVyM7PAyAN8x9RFC7A5Ou1o/pztF+/iUWrVuHhHx6weS17gygzM9MT3RE0zsrAfI/H2a93LjyI3L2mtf57m0cOn+9BYWEhenp68Os//wm6xYuhiomFZmAAMpkcnQ2NGOzqht/QEHz1eqgWpyEuKxstlVdQXVODaFUCImPjZ68lptQxlhBF4SKOvl7jBodBZ+dg299/n9H1rA2iAwcOeP0ehasyCAoKcuqrmQsPIjauaa3/Uo8dsYTv98DPzw9J2TmIylyMnuZm9LW0QG8woLutHT4LQxGXpEJkyiIERUYCABJzl+FWbw+aq2vnKAoxm/+IonADW1+v4eHheOm//hNJFjbkyckJDPX1waDVQabwRVhUFPz9AwCIexCJHS48iLj0SvL22BFPYtoPSspdjpWbNs2+w30dXTCAQtz9a7EwIWHOOXJfBYITEtDR3Iz7Jidm33FAvOY/oihYwPLrVa1Wz7Eh9/d0obm6Fh0d7ZiiKBgxkztFSdNITExCSs5SRMbGzxtE69ev56M7gsITMuAi+yxb17TWf6nHjljC53tguR/k7x8Af9UiGGlA2dsNZbB1WYbEx6KvpQVDfX3wN1PmYjX/kVxPHGBuQ66pqMSHpWegvtMLOiUFUQ+sQ8KXNyLqgXWgU1KgvtOLD0vPoKaict4gOnToEK/9EAJcy4CLnD5sXtNW/23lB7OFmGJHLOHzPbC1H+TrK4cPTUGv1Vs9TyaTwwjAoNXN+b1YzX9EUXCAyYZcU1GJm2o1KJUKS762FUkrcxESE4vAheEIiYlF0spcLPnaVlAqFW6q1aitvDpnEOXn5/PaDyHAtQy4yD7L5jVt9d/kkWNoaETl2XPzMg+b0GqnUXn2HAwNjSgUaX4wPt8DW/tBIeHhUILGaF+v1fMMBj18AMgUvnN+L1bzHzE9cYBKpQI9NIwbvb2IXr8eyXm2a/7KfRVIzluNFgA3Ll7ECj+l6AaRmOHCg8hTXklsx44Q5mNrP0ip9EdiXBxqe/tgWKKDTD5XIYx09UBJ0wiLipr9nZjNf0RRcEBQUBCUWi2mtDrE5TDbsIrLyULbRx9DSfnMDqLAwEAumykKXJGBM4FtXHgQsXlNR/1nI3ZE6PD5HtjbD0pOT0VbVze61PVIWvZFihG9Touxzk5kJSbN2cgWs/mPKAoO0Gg0mPb1RaC/AncamuYMIlvcaWhCYGgwpvU0NPe8nsrLyz3QWmHjjAxcCWzjwoOIzWsy6b87sSNigO/3wFY2hvCoGGQuTkd1QwPaAcTfC8TrqLoJ/2ktUnKWApBGeQCyR8EBbW1twMIwLFu2AsbeHrTfrIZBr7N6rEGvQ/vNahh7e7Bs2QpgYdisjfqxxx7zYKuFCVMZlJSU4LUjR1Da1gZtZgZStxUga9dOpG4rgDYzA6VtbXjtyBGUlJTMOc9ehUFbOKowyOY1nRkDJu+73NxcZGVlSUJJAPy/B/b2g5YuX46c9HT49PTg1kcf4+of/4SxmmokxsVAp9Xh2vnzKC8+CtntBtHkF7MGWVFwgMlGnbViOcJ7e1F3uwGNA/0IjInCgqgY+MgoGA0zG2HjvX0I1OmRtTgdkdGRqGtpmbVR19TU8NsRAcBEBu4GtnGRfZata5IxIAwZ2NsPCg0LRbhCjpGr12Do7EZQZDiMfko0tbZLxvxHFAUHmNuoly5fjui4mJmMk53dGOjsgZGi4UNTUILG0rg4JKenIjwqBt1traJ0neMTNgLbnMnfxdSEwMU1Cfxibz8oHMCWDZuwbNkyyOVyyZn/iKLgAEsbdXhUDMKjYpBtkcM+JDwcSqX/7HmWNupjx47x0XxB4UgGbAW2ceFBxMY1yRgQlgykvh9kC6IoOMCWp4RS6Q9lfILVc6y5zpWVlSE9Pd1j7RYi9mTAdrptLjyI3L0mGQPClIGzucTEDilcxBGNjY147cgRGByYRExUnj0H2e0GPLVv3+ykQQoX2ZeBWq3Gq8ePIXVbgVPeRd1trWgqLcMzu/fYfNmFUo+CjAEiAzcghYuEjlBt1FxMgHzBZWAbF1+M3vYVSpAORFFwiLs26qeffpq1tnBRkMcT2JOBo8A2W9l6xZRvh80xIFaIDPiHKAqOccdGHRsby0obuCjI4ynsycBWYJujbL3jY6OIhDjy7bA1BsQMkQH/kD0KD+Ks2YcN2+xsjEF6GiPzl9CCghzJoLi4GKVtbdhQtBcKhR9qKipRXVODSb+ZmgAh8bGQKxTQa7UY6erBaHsrBq9UoVC1CK+//roHe+IaxD5PZOAGZI9CjHjaRs1l8RyhYB7YFrhgAW6q1ZCpVFiSuwxyX8WcY0NiYtECYKKzG+16PUpKSgSlFAneiRj2DYmiEDCbNzv2lrIHFwV5PI0jGZicBo6c/SsqxscR9aX7rWbrNeh16FLXgxoYwFe2bcNYX78glaLlpEGKV7n/HggVMe0bEtOTgNG4URJVo9Hg4CsvQ5uZgZWbNjE+79r581DU1eOFn/xUEF81TGXw5JNPoqyrEwvvX4sFCfE2U6VkLk7H0uXLodVOo7z4KLapVCgqKvJAT+xja9II0OmxdulSQU0ansad90CozNk3vOfkYr5vaHJycXPfkJievIGNGze6bJu1LOHIFKHV9GUiA41GA0NgAFZu/BKCIqMcpkoBhFWj3J6zwdHf/hZ329oE62zgCdx5D4SIu7nJ+IAoConiqeI5QsCkFBevWIE41SKHqVJMCEEpOpo0IhapsGHvXkFNGgTXEeu+IUkzLmAiIiJcPpeLgjx8wEQGlkpRqfRHdHwCEhYtQnR8glUlAfCvFGcnjfQ05G3ZbNUjzd8/YHbSkKWnoeTyZTQ2NvLQWv5w5z0QGqZ9wxVO7BsOKZW4dOkSxy2zD1EUAubMmTMun2seY+AMQqvpy0QGYlWKTCaN3bv3zP4slEnD07jzHgiJ2dxkqcnO5yZruA2NRsNxC21DFIWA2b17t8vnclGQhw+YyECMSpHppHHq1MnZn4UyaXgad94DczQaDdRqNaqqqqBWqz0uQ5OJNNaFfcPhe+fzBdmjEDDumhi4KMjjaZjIwF5dY1vwXeieqbPB0NDdOf8Xwr6Kp3H3PRCKG6qY9w2JopAwQk1MyAViU4pinjTEhJDS14jVRAoQRSFoTp486fggB3BRkMeTMJWB2JQi00nj4W88POf/Qpg0PI2r74HQ3FBt5SZzhBD2DYmiEDDHjh3DM8884/Z1uCjI4ymckYGYlCLTSaOmthZr89fO/p/ppCGGtBBMceU9EKIbqhhNpCaIohAwJ06cYEVRAOIt4eisDMSiFJlOGvV1XygKJpOGUOzxbOLKeyDU9DViM5GaIIrCy/CG4jliUYpsTxpc2+PFskphu0Qum4jNRGqCKAoP48zL9txzz3m4dcLDHRkIXSkymTTuf2A9o0mDS3s836sUZ8eA0NPXiMlEaoIoCg/hysumVDrnESNFpC4DR5NGU1UV2sr/ZnfS4NIeLwSvIWfHgBg8ysRiIjVBssd6AFczRZKCLd4jA1sfEuf++Ee8/OxBu5OGZfEmRzDNnCuUolfOjgG1Wo1Xjx9D6rYCp7yLutta0VRahmd27/HoSpRDkx7JHusqnrazCs1FjyBMbO2r3DxdYncy12g0uKSuAZWYgL7uHrtJEE0wsccL0WuIKWJzQxW6iRTwIkXBh53V3Zdt+/btrLZHjHibDCwnjZ07d9o8trGxESdOnMDfatUIDgzEnatXZtOqJ1qkVbfEkT1eSF5Dzo4BMbuhChWvUBR82VndfdkOHDjAWlvEirfLwFb/TWO6eWwUSEhExMrlWBAZCb1Wj9G+XtT29qGtq3u2UJMl9uzxQvMacmUMiNUNVahIPimgyfRjWJyODUV7sXLTJsSpFiE8JgZxqkVYuWkTNhTthWFxOt6rqEBJSQkr92UjU2RBQQErbREz3i4Da/03H9N539iFiIR4+AcFISAkFAsiI5CQlY20DethjI1FdUMDam/cmHcNexHeQkte58oYMHmUGRoaUXn2nM3EmFrtNCrPnoOhoRGFAnBDFSqSXlHYM/1MTk5gqK8PBq0OMoUv7lt/Pz4He3ZWNlz0CARLLMf05OQElDSNka4ehMTEzh4nk/siaVkO2gHU3W5AdFzMHDOUPXu8GLyGmGDNo2xhTAzGx8YwPTmNoZ4uaAeHEEXTgnFDFSqSVhTWTD/9PV1orq5FR0c7pigKRswsq5Q0jdjYGHRPT7NiZ2XjZUtISHCrDVLA22Vg2X/LMe3vH4DExCSoOzuhvy8bcl/FnOPjszLQONCPloamWUXhyB4vtOR17owBkxvqyZMncfZPf0b35BT0Sj8AFHz1WiT4+iF17VpkZGSw12AJIllFYc3OWlNRieqaGkz6KRCckoKo+FjIFQrotVqMdPXgVmcnxkaG8aczZdi1a5dbdlY2XrZTp065fH+p4O0yMO+/rb2DlJylaG5vR0fVTSTnrZ5zvkzui8CYmTri2VOTUCr9HdrjheY15O4YqK+vR9PwMBZk5yA8KgIRsbFQBgbA398fA13duNrUgqYjR7y2JjkTWN+joCjqHYqiLlEU9TMbfw+jKOoDiqIuUhT1G7bvb8LSzlpTUYmbajUolQpLvrYVSStzERITi8CF4QiJiUXSylws+dpWLLgvB7fGJ/C73/3OrfuzUUxnx44dbrVBCni7DMz7b2vvIDI2HjnZ2TC0t6Ol8gr0Ou2cvy+IisEUKPR332Fkjxda0St3xoD5fs5Xvv8YHvzGN7B83TpkLFsO1eIlnO1RSg1WFQVFUbsAyGiaXgcgjqKodCuHfQfA72maXg8gmKKoVWy2wYS56ae/pwvVNTWQJSUhOW/1vOW5CbmvAkmrciFXJeHCzRtuFUxh42Xr7Ox0+f5SwdtlYN5/e+bM7Pw8LMvKAt3WhlsfnEH7tSqM9PZA098Pzd1B9DU3o+LPf4LsdgOjwLh169YhbGoK1y98wqidXHoNuToGmNQkB8BbTXK+K+45A6uR2RRF/RrAGZqmP6Ao6mEAwTRNH7E4Zi+AxQB+CeB9AA/TNN1n5Vr7AewHgL17966sr68HADzxxBPIzMycdZlbv349Dh06hPz8fABAYGAgysvL8dBDD+FaXy+iHrgfmckpuNndBeXSpZDJ5YiPj4dC4YuWllYAQGhoKBIS4lFTo4Z2bAxj6jos9vHBwCcXYTQaAczkxD927BhOnDgBYCb/jFKpxLPPPgtgxtf7wIEDsx4aCQkJeO2111Dw7b3wyVqKiNRkPProbly7dg2NjQ0AgA0bNkFv0OPTv10EAPhNTiNhbBxVH34IpVKJ5uZm3L17F1u3bsXAwAAA4MKFC3jhhRdw7tw5AMDrr7+Onp4eHDp0CABQVFSEgoIC7NkzU2s5Ozsb7777LjZs2IDx8XEAQEVFBZ5++mlcvDhz38OHD6Ourg5vvPEGAGD//v1Yu3Yt9u3bBwBYvXo13nzzTeTl5cFgMEAmk6GyshKPP/44rly5AgA4cuQILl++jLfeesup5/TYY4+hpqYGwEw66bKyMhQXFwMAnn76aTz33HMIDg4GAGzevBkHDx7Exo0bAQARERE4c+YMdu/ePftyu/KcTp06hR07dsxOSGVlZTh8+DBOnz4NAHjxxRcxNTWF559/HgDwyCOPYM+ePbMxDmlpaTh+/Dgnzyk+Ph6JiYkAgKeeegq/KS3BaEwU/MNCsXx5LuLj41FaOtPO2NhYrFy2DL87/BtMTk0BSn+okhehs7kFd698Bv/+Qbz99tvo6upi9JyGhoagj47Clh/8AD2aMQyNDAEAHnpoB5qam1BTXQ29zoAIPwWolnbUffghwsLCWH9OdXV12Lx5s9PPKTU1FVPpqej19YXcV4awsIXYsWMnjh8/hsnJCQDA3r3fwaefforW1mbodQaEjI4ifUqLyspKzt6n9PR0FBQU4F9eeglU6AJQMl98edNG3Cwvx3B7B4KCgnDs2DG336erV6+yFpnNtqJ4B8CvaZq+SVHUFgC5NE2/bHGMCsBLAOoBJAD4IU3TOgeXdrqRGo0GB195GZqURejq6gadkoKklbkOz+tU18CnsxuxwcEIbG7BCz/5qVtLaXfSIPT39yMyMtLle0sBb5eBef9NY1qbmYGVmzbZPc/cq+/WtWsI7enFa8/93OmxbC39jLXkdVza910ZA87Iypxr589DUVfv9ntvC1fT+bgIa4qC7T0KDQBT7oAgG9d/EcA/0DT9PGaUxT6W2zBz83umn67PqzFuMCAkPtbhOQa9DuO9fUiMi0NSZgYrPuGFhYXYlZ8P2e0GlBcfxbXz59Hd1or+ni50t7Xi2vnzKC8+atUkcPjwYbfuLQW8XQbm/XfGnOnvH4A41SJEq5JAayawLjvHpYmvsLAQT+3bh20qFRR19WgqLUPdyffRVFoGRV09tqlUeGrfPk43gV0ZA0KLBQH4i+liA7a9nq4BeABABYBlAG5ZOSYAQA5FURUA8gB8yHIbZlm3bh3+cvETtE1MIlFhfV/CnC51PQJ1eiSnp8JoMLDmE+5qpsjTp0/j5z//udv3FzPeLgPL/vMRccx3fQ9XxgAfsSD28siJOXcWwL6iOAXgIkVRcQAKAOymKOoXNE2be0C9BOAIABWAywCOsdyGWdLS0vCVFblQn/0r2j+vweIH1kEm9513nEGvQ5e6HsbeHmQtTkd4VAy621pZ9Qnn+2UTAmIpfCNk+Cx8I4bkdSY8GQvCJI+ckHJnuQKrioKm6VGKojYC2AzgVZqmewHctDjmMwAeG21FRUU48+mnaK9Vo9FgQGBMFBZExcBHRsFooDHa14vx3j4E6vTIMsuLw5VPuDMv24svvsjqvfnCnYSMUpGBq1jrvxgL37iDK2PAU7EgTPLIXaiuxkB3N+K3FQgid5YrsB5wR9P0EIATbF/XVYKCgvDwV7+KE3U1iAkNRU9nNwY6e2Ck6NlMm0stMm0KJZPk1JRzX0NCxN2EjFKQgTvY6r/YCt+4A9MxYLliXZqQiI85zCDLtITAx3/8I2433EbYlHPmLE9V3GOCZCOzzTHZdQ06PQq2F2JkcBA6nd5m7n6hZJJ8/vnn8fWvf53XNrgDG7U4xC4Dd7HXf28xZzoaA7ZWrPTQMAZ6e1H+fjA2P/xNh/dx5r13Zs8ha00e1A1NaOnuwtK+Xpup3y0RUu4sr1AU5nbdmxBPQXMxI/bNOzEhpr0DtnG0YtV9fB41fz0Hzd1hbHusiLX33pk9B5nCFxFxsZjwkc3JueUIrnJnuYJXKApAnHbdRx55hO8muAxbm3dilgEbeHv/AdsysLVinZycwHTfFKISEhDxnW/j8gelaP3sCv7Q24ul6x9w+713tl5HWFQUFLQRdyc0qK+tRXRsDCLj4uxWIQT4q7hnDa9RFID47LqmaFCxwWbhG7HKgC28vf+AdRlYW7HazAwdvAAL4uMw3dKMCR+Z2++9MyUETG0aaG3HEE1DExoK3SefIDQwyG4VQqHsk5rwKkUBiMuuu3PnTqeKygsFNmpxmEwpYpUBW3h7/wHrMrBcsTrKDA25D+4ODiIIwIHde9x675nGaJi3KSQvD+PNzdDJZAjJyYJRq7NbhVAo+6QmvE5RmPBmuy7XSKXwDUGYWK5YTZmhZSoVluQum5f0MyQmFvr7snFDb8QnlyuwsbYW3/ym481tWzCJ0bDWJh+FAs2XKzDa2YX0Bx4AtWQJutT1qG6Yyf22dPlywe6Teq2iEANCGSTOwmawk1hlwBbe3n9gvgzMV6yzmaFVqnm1OMyR+yqweONGqPv68eePP8KKFStclq2jGA1bbfIPW4DYBQsg7+nDrQ/OIDghASHxsegZHcGVzyrQ19oC3cCQoPZJTRBFIWCOHz/OdxNcgs1gJ0/LQGjR42IdA2xiKQPzFWvj59WY9FNgSe4yh9fxkVEIX5SEoc5utyKeTTm3Sm3EaDRX185rkymP3Ip1+VClLJrZS2luRl9LC2iDAXfU9Qi8qca3duwQ1D6pCaIoWITtSWbr1q04c+YMiy30DI5eJGvY2rzzlAzciR7nErGOATaxlIFpxTo6NISOjnYEp6TYrDFjjtFAQy5XICwlGdcabrsV8Wwr59bk5ITVNpnnkQuPikFkbDzuM8/wGxyM0J5etytrcgVRFCzA1SRjqm0gRthKXucJGbgbPc4lYh4DbGEpA9OKtenmTUxRFKIYZIYGgNG+XihBIz0nB+13PnYr4tlWzq2hvr45bbKWR86Ev38A/O+tuGUKXzSVlgkiCtsaRFG4iZAnGT7hM3mdM7ARPU7wLKYV6++vXoE+IhxyBpmhTaafpXFxCAgOYsVpwlpsFnzkmBzXYFKjwZi6xmoeOWsI3ZGDKAo34HqSuXDhApvN9ThsBDlyKQMxRI+LfQywgTUZrFu3DiWXLqGzswtxWu38kywwN/1MT06xFvFsGZvVdncQw9098FEGYGFoyLw8crYQUhS2NdguXOQ1eKIe7wsvvMBWc3nD3cI3XMrA5Iu/wono8SGlEpcuXeKsTZZIYQy4izUZpKWl4RubNsGnvQO3L34Kg956kUyDXof2m9Uw9vYg857ph+2I57S0NBQVFeGFn/wUz353H3Ijo6BaGIaC7YVYdf/9jFJ2CCkK2xpkReEinsgvf+7cObz00kvuNFMQuBPkyJUM2Iwe5xKpjAF3sCWDRx99FJWVlSirrsGtBcEIiI6Cb0AAQFMARUM3MYHpwaE5ph8uI56DgoKwZs0aFNTXo7StDT4+zL7DhRaFbQ2iKFxALJOM0BBSkCOb0eME/jhw4AAann8e7VXXMRAQCK1CAZqiQNE0FFotFsXGImddPhYvnXlWnoh45qMKIdcQReECnppkXn/9ddcaKCG4koFYosfJGLAvg/r6euh1OkzodQhITkbK8vvg6+cHWk9jamIMkwN3UV1Ti6nxcUwOjXjEaUIsjhzOQBSFC3hqkunp6XG+cRKDKxl4slSmO5AxYFsGJmeShK1fRYq/EvW3bmPkVsNsxHNQ6AJowyPQdLkCTcdOIJWi8MNvfcsjXmtizFZtD6IoXMBTk8yhQ4fw6KOPOt9ACcGVDDxVKtNdyBiwLgNrHmsJaSlzIp5N2WNjAOjiYhA6pUVGRobH2i22bNX2IIrCBcQyyRBsw2b0OMHzWHMmiYyNnxfxLFP4IiwqCjKZDOXFR91K3eEKYspWbQ/iHusCpknmblMLtFpmZiTTJLPSiUmmqKjInWZKAi5lsG7dOoRNTeH6hU8YHc/HpiMZA/NlMOtMkppsVcH7+wcgTrUIienpiFMtgr9/wBfOJA23odFoPNX0WUyOHLm5ucjKyhKVkgCIonAZT0wyBQUFrjZPMnApA9Omo6GhEZVnz9lU+lrtNCrPnoOhoRGFHt50JGNgvgxMziSxLjiTDN87n+AcRFG4iCcmGVLdjHsZFBYWYld+PmS3G1BefBTXzp9Hd1sr+nu60N3Wimvnz6O8+ChktxuwKz/f45uOZAzMl4FYPNakBNmjcAOpeTZ4K1LadPQGxOKxJiWIonATLieZ7OxsllsrPjwlA6FuOpIxMF8GxJnE81A0TfPdBiaIopFCK3pDIEiV4uJilLa1YUPRXsYea+XFR7FNpfImBwGKrQuRPQoWYduzYcOGDSy1TLx4uwy8vf+AdRmIwWNNShBFIWDGx8f5bgLveLsMvL3/gHUZiMFjTUqQPQoCgSBKiDOJ5yB7FAJGr9dDLvduXe7tMvD2/gOOZSDUeucCgLU9CqIoBMyPf/xj/PKXv+S7Gbzi7TLw9v4DzGVAnEnmwZqi8O5PFYFz8eJFvpvAO94uA2/vP8BcBkKqdyI1iKIgECSKN31he1Nf+YAoCgFz+PBhvpvAO94uA1f6LzWbvT0ZSK2vQoUoCgFTV1eHNWvW8N0MXvF2GTjb/5KSEpRcvowhpRILMzOQmpIMPz8lpqen0NPcgtKmFly6dQuFIvICsiUDKfZVqJDNbAGzatUqXL16le9m8Iq3y8CZ/psqvsnS0xiV3+QjyaErWJOBVPvKMmQz2xmI/ZIgdaxVfLOGQuGHvC2bUYmZ+IOMjAzRmWa8qa9CQdKKQuz2y/379/PdBN7xdhkw7b+1im/2WLHxSyhv7/B4xTdXsJSBlPsqVCSrKKRgv1y7di3fTeAdb5cBk/7PVnzLzGCUIA/AFxXf6uqxS6MR9ArbXAZS76tQkWSuJ5P90rA4HRuK9mLlpk2IUy1CeEwM4lSLsHLTJmwo2gvD4nS8V1GBkpISvptslX379vHdBN7xdhkw6b/UK76Zy0DqfRUqklMUs/bL9DTkbdls86vDZL+Upaeh5PJlNDY2erilBAI7eFPFN2/qq5CQnKIw2S9XOGG/HFIqcenSJY5b5jyrV6/muwm84+0yYNJ/qVd8M5eB1PsqVCSlKGbtl6nJztsvG25Do9Fw3ELnePPNN/luAu94uwyY9N+84psziKXim7kMpN5XoSIpRSE1+2VeXh7fTeAdb5cBk/4HBQVhZfpi3G1qsVmXwRKtdhp3m1qwMn2x4Dd3zWUg9b4KFUkpCqnZLw0GA6vX02g0UKvVqKqqglqttrmCYnqcJ2BbBmKDaf+lXPHNUgae6quQ3gO+kZR7rNTslzKZjJXrMI0nEWLcCVsyECtM+2+q+PZeRQUqAUbRyg+JpOKbpQy47qsQ3wO+kVQKD41Gg4OvvAxtZgZWbtrE+OLXzp+Hoq4eL/zkp5Jbms6JJ7lXAcw8nsRUASxKJkOfweDwOCHHnRCsP29rFd+k8By56CvT90Uk8iOFi2xRXFyM0rY2bCjay2hDW6udRnnxUWxTqVBUVORWI9nm8ccfd2szl2k+nPf/5x20Vn+O1NyV2PZYkaDy5rgrA7HjSv+l9kVsTwZs9lWC+aNIridbrFu3Dpdu3cL1C5/YzQNjQsi22itXrrh8LtN8OCODA9D5+UG5chWMISEYGx5CeFTMvOP4ypvjjgykgCv9T0tLQ1paGnZJJMeZPRmw1VeSP8o+ktrMBr6wXxoaGlF59pxNzwitdhqVZ8/B0NCIQpHYap2BaTxJc3UtJv0UuG/7Noz7ytHS0GT3eCHHnRDmYqr4lpubi6ysLFEqCaa421cpxV9xgeRWFABml4Mlly+jvL3Drv3yIQHbGo8cOeLSeUzz4UxOTqCjox3BKSnwCwhAYEwUOjq7kT01CaXS3+o5ns6b46oMpIK39x/gXgYkf5RjJLeiMFFYWIin9u3DNpUKirp6NJWWoe7k+2gqLYOirh7bVCo8tW+fYJUEAFy+fNml85jGkwz19WGKohASHwsAWBAVgylQGBkctHr85OQEuttaAR852u4Oora21qX2OYOrMpAK3t5/gHsZSC3+igtYX1FQFPUOgEwAH9A0/Qs7xx0GUEbT9Gm222BC7Lbat956y6U020zjSQxaHYwA5AoFAMBHRsFI0dDp9HOO6+/pQnN1LTo62jFFUZgc12C4uwe/fPttFNTXc7o56qoMpIK39x/gXgZSi7/iAlYVBUVRuwDIaJpeR1HUYYqi0mmabrBy3HoAMVwqCXNM9ktvgWk8iUzhCx8Aeq0WAGA00PChKfj6fjEsaioqUV1Tg0k/BYJTUhAVH4tJjQY+ygDQC8NQ2tYm+HTtBII9pBZ/xQVsryg2Ajhx7+ePATwAYI6ioCjKF8D/APiAoqiHaJr+i7ULURS1H8B+ANi7dy/q6+sBAE888QQyMzNx4MABAMD69etx6NAh5OfnAwACAwNRXl6Oxx57DDU1NQCAY8eOoaysDMXFxQCAp59+GrGxsXjyyScBAJs3b8bBgwexceNGAEBERATOnDmD3bt3z2aVPXnyJI4dO4YTJ2a699xzz0GpVOLZZ58FAGzfvh0HDhxAQUEBACAhIQGnTp3Cjh070NnZCQAoKyvD4cOHcfr0jH588cUXMTU1heeffx4A8Mgjj2DPnj3YuXMngC8CjbZu3YqBgQEAwIULF/DCCy/g3LlzAIDXX38dPT09OHToEACgqKgI69evx7k//hEVd3qQsXo1thdux+9/XwydTgcA+O539+Hjjz9CS1MTehubYIyJhVYmw+3PrmKqsRFtiQmgfBU4+t//Dc30NEIyM7Fy6xbU1dVh+E4fxrq6kBwagkmFL3qNetzo7UbfX8/gzJkzqKioYPU5bdmyBatWrRL0c0pLS8Px48edfk4FBQXYs2cPACA7OxvvvvsuNmzYgPHxcQBARUUFwsPDZ/t/+PBh1NXV4Y033gAwU9Bn7dq1s2m4V69ejTfffBN5eXkwGAyQyWSorKzE448/Pus5dOTIEVy+fBlvvfUWq8+Jy/dpcHAQO3bs4Ow5vf322zhX9gEq7vTg0e//HTQaDSorZ8xd2Tk5SE1JxV/+cmqmL5FRs+9TT0Mjxj75G371z0/hxz/+MS5evCio58RmCWFW4yjumZ1+TdP0TYqitgDIpWn6ZYtjvg9gG4ADAJ4A0EvT9BsOLi2KYA+2+eyzz6wWlWcC03iSyrPnoL7Ti7TND6L1cgWWRkVh1f33o7+nCx+WngGlUiE574vsnQa9Do3lF2ePM7+O7HYDntq3j1UzlDsykAJi7T+b5Yc9IQMpxV+ZwVocBdub2RoAJneZIBvXXwHgLZqmewH8HgDzEGovw/T14ApM8+Gk5CyF/7QWn58uRaBOj+T0VABfuM0m5i6bc3yXun7OcSa4chd0RwZSQGz9b2xsRHFxMQ6+8jJePX4M/3HyPbx6/BgOvvIyiouLXar74gkZSDlXFhuwbXq6hhlzUwWAZQBuWTmmEUDKvZ9XAZC+ywAPMM2HExIeAd/paUxVfw6f3JUIDg2b4zYr953Z6DbodehS18PY24OsxenzgvK8zV2QMB8xlx+Wcq4sNmBbUZwCcJGiqDgABQB2UxT1C5qmf2Z2zDsAfktR1G4AvgAeZrkN82BzGexJ1q9f79b5ppfxvfJyvH/jOoLjExCTrEJ4dDR85PLZeJJFNLBm2Qr0aXUoLz4KKigAg2NjiAoKxGj/AEb7ejHe24dAnR5Zi9OxdPlyq/eLTUlGU1092traWHMecFcGYkcs/Z9Nf7E4HRusTLJxqkXQ3r8O1y98gvfu7WMxVRaekoFU4q+4gPVcTxRFhQHYDOCTe+YlNnCpkWLPeaPX6yGXu67LTf3/qLISzXcHcXdCA72PDLJpLcIDA5EcHYMH16yZlz32g4sXcX1gABFrVkIZGAwlaCTGxSE5PdVqeg8T/T1dqDv5Pv5p5y7k5ua63G5z3JWB2BFD/xsbG/HakSMwOEh/YcLZ/SxPy0Ds84YZws31RNP0EL7wfOINMS+DTeTn57vsuTCn//evxeZ7wUSDd/rQ29KKsc5O+Pr4YOHChbOD3hR3kpGRgRf+93+RuHgJYlVJCAkPtxmpbQ4X7oLuyEAKiKH/pvQXG5xIf1He3oFLly4xmnA9LQOxx19xgbA/VVyEy2WwGLDX/zjVIuSsWTNra7XW/6VLl2JRaAi009OIjk9gfF9SbtL7kHL6C2+Lv7KH5FJ4zGaBTE9D3pbNNgevKQukLD0NJZcvu+SNwTWBgYFOn8NG/4VUbtIVGUgJofffE+kvhC4Db0ByikJKWSDLy8udPoet/gvFXdAVGXCJp8tjCq3/lngi/YXQZeANSMr0JLVl8GOPPYZ3330XADPPLTb7LxR3QXMZ8AlfG5xC6b8tPJH+Qugy8AYkpShMy+BUF5bBbLt1skFNTY1TExTb/ReCu6ApHQGf8OkYIYT+20OlUiEUM/tTcapFjM9zZj9L6DLwBiSlKKSWBXJoaAivHTnCeILiov+FhYXIyMiYUVZ19Wiqq5+jrLaJy13QabzdMcIRpv2s0qYWaO9fxzj9xd2mFmxjeT+LwB2SUhRSygJZUlKCFdsLYXBigkpOTuak/3y6Cx47dozT69tDCOUx+ew/U7guPywGGUgdSW1mmy+DnUFobp2mCWo4JMQpzyW9Xs9p//korVlWVsb5PWwhBMcIPvvPFK7LD4tBBlJHUopCSG6d7mCaoMZ8mAVWmiaomzdvSqL/5phSWXuaWceA1GTnHQMabrPmDcVX/52lsLAQu/LzIbvdgPLio7h2/jy621rR39OF7rZWXDt/HuXFRyG73YBd+flOmefEIgMpIynTE8D9MphrzD2XOlubGZ1j7rn0vYe/6dH+izWPliOE7hghRLl7+36WlJGcohCKW6ermE9Q/tHRjM8zTVByudwj/feUu+jTTz/t9jVcQSiOEZb9F3oeIi72s/gaA4QvkJyiAITh1ukq5hNUEMXcMmg+QXHdf0+6i8bGxrp1vqsIxTHCvP9iyl/GZvoLvsYA4QskqSgA8S6DzSeojz7+CPv2fZ/ReZYTFFf997S76JNPPslLUjxPxAcwwdR/b3bT5WsMEL5AsooCEGcWSDY9t9juvxDcRa3Bhb2er/gAy77o9XrByp3gPUhaUZgQUxZI8wkqMZFZ5lZHExRb/ec6nbQ1Nm+2PTFyba/3pGOErb4oIsLx6quvojs8FF/zoNyFhL0xQPAMXqEoxIZpgpoyGhkd7wnPLb7yaB08eNDq7z1hr/eUY4S9viApCZ+UfgClvwKNtXU2qwuaw3f+MrZXeLbGAMFzEEUhQEwT1A+efx6+MrkgPLf4chfduHHjPPu0J+31nnAMsNeXU3/+MyJXLAMdE4PqhgYAYKQs+MhfxtUKz9oYIHgWoigESmFhIZT/+I8zAUwC8NwSirsoH/Z6rhwDmPTFYKRByeVIvC8bgx3dqLvdgOi4GLslaQHP5y8Tk0cWwXmIohAw6enpeGrfPkF4bvHlLhoRETHn/3zskwDcOEYw6Yt/YCB8AOi1WsRnZaBxoB8tDU0OFYUn85dxvcKzHAMEz0MUhYA5c+YMAAjCc4svd1GTDABh1BthyzGAaV/2PvZdlP7fMYx09SAkJhaBMVHo6OxG9tSk3Trmnspf5okVnvkYIPCDpHI9SY3du3fP/sxHQj5z+MqjZS4DT5Td9BS2+jI1NYk7XZ3obG3Fna5OlJ4uQWJiEsY6O6HXabEgKgZToDAyOGjz2p7M3+WJxInmY4DAD2RFIWCEVsebjzxa5jIQyj4JG1j2ZbCvFy0NTejo7sYUKBgpGj40hZZbt7Bw2X2ghofQUXUTkekpMFI0dDq9zWt7Kn+Zp1Z4QnsPvBGiKAiM4TuPllDSarCBeV9qb9xA3e0GjPvKEZgQh4ioGMgVcui1evTodeg0GDE9pcVE1XVMjgzDV6eDr+/8V9fT+cuEnjiRwB5EUQiYkydP8t2EeXg6j5a5DISSVoMNTH258tHHmAoOhk9sLNKyMiCT+845bvWDX4ZMJkNXRCT6PvsMvX+7CD9QaAlbCINez2v+Mk+t8IT4HngbRFEImGPHjuGZZ57huxnz8GQeLXMZSKnsZlBQEBIWhODs1SuIf+jrSFqWY/W4/oEBxMcnIGlZDgx6HUYaG5Hjp0Rgcwuamlt4zV/mqRWeUN8Db4IoCgFz4sQJwb4gnsqjZSkDsdcbscSg14PW2f6yHhwYRHz8TCoXWjcNuUyGVatW4fHHH+c9f5mnVnhCfg+8BaIoCG7h6TxafO+TsIVGo0Hn6AjSs7Mw0dGNFuoKEnOXQe6rmHesXqdFR9VNGDu6kZ6dhc7REQDg3b4vpRUewT5EUQiY5557ju8m8I41GYi53ogJ00ZwXsFW3O25g+qaGtzq7UFwQgJC4mMhk8lhMOghGx7BrQ/OwH9ai2XZ2VgYG42m0jLBbAR7YoVH3gP+IYpCwCiVzm0SShFbMhBLvRFbCfLMN4Kz8/MQrUpAc3UtOpqb0dfSAiNmgpzokVFkZ2UhJWcpImPj0d/TJShXX0+s8Mh7wD8UTdN8t4EJomgk26xatcrrk6ExkYEQ60c7SpAXGRmJ45c+Req2gjn2/cnJCQz19cGg1UGm8EXpmTP4+x/8w+zfu9ta0VRahmd27xHEisLEnFxPdlZ4ruR6Iu+By1BsXYisKAiiR2j1RpgkyAusrsZQd/e8jWB//wD4m/1frpjrLitEV19APCs8gmsQReEmXH7Nbt++nZXriBmxycCZBHnDLc0Yufg35NjZCE5LS5/9WegbwVx5woltDEgRYnpyEa6rqwFAf38/IiMj3W6rmBGTDBobG/HakSMwOEiQZ+LcH/6A2jN/xdJtBdj88DetHjMxMY6AgEAAQOXZc5DdbsBT+/Z51Ze5mMaAwGDN9ESSArpASUkJXjtyBKVtbdBmZiB1WwGydu1E6rYCaDMzUNrWhteOHEFJSYlb9ykoKGCpxeJFTDJwNkHehp07EJaQgDt/u4TKs+esJlv8wx+OQ6udRuXZczA0NKJQgK6+XCOmMSBViOnJSTxZXY0gHlxNkJf15U3oKimFUa226uo70NqG8uKjgnb1JUgfoiicwNPV1RISEtxorTQQiwzcSZA3kZqKh9fdj/7+/nkbwYZrN7DtgQ1evREsljEgZYiicAJPV1c7deqU0+dIDbHIwN0EedHR0SgoKJi/EfyTnwpy49qTiGUMSBmyR8GQWdNCarLzufcbbkOj0Th9zx07djh9jtQQiwzYSpBnWaDq29/+NvuNFRliGQNShigKhvBRXa2zs9Ppc6SGWGRgniDPGRzFRYil/1xCZMA/RFEwRErV1Qjsw1epWALBExBFwRA+qquVlZXN/qzRaKBWq1FVVQW1Wu2SKUuMmMtA6Kxbtw5hU1O4fuETRsczSZAnpv5zBZEB/xBFwRCuTAv2OHz4MBobG1FcXIyDr7yMV48fw3+cfA+vHj+Gg6+8jOLiYsnXEz58+DDfTWCMKUGeoaHRZlwEAKfiIsTUf64gMuAfEpntBMXFxShta8OGor2Mc++XFx/FNpUKRUVFTt8vNTUVm3fvnpNozTxnkDuJ1sSCGBPCsZkgT4z9ZxsiA5chSQH5wJPV1UpKSjAVHg6Di4F9Qsyo6i2QBHkEqUFWFE4yG5mdnsYo9/6u/Hynv/ZNOYN6w0JQuHevw+PNcwAB4DwHlSc5e/YstmzZwnczXMZdhS32/rMBkYHLkBUFX3iiupopsC95+X2MjjcF9h0+fBgT/v5201tfunVLVKaqqSnnnAeEhrsp0MXefzYgMuAfsqJwEa6yx2o0Ghx85WVoMzPweWsz9u37PqPz/vLb36Ll4qdYuWsH8r66hZOVDh94u33a2/sPEBm4AVlR8A1XuffNcwZ93trM6JzBvl4M67UwJiUi9b4cmxvtbOWgIhAI3gVRFG7CdnU188C+jMyljM5paWjClF8gFibEw6DVOTze3RxUnuSRRx7huwkew9p+hjf13xZEBvxDFIXAMA/sy17qWFFMTU2io7sbyogwyO70QWZROtMaszmo6uqxS6MRtDfUnj17JO/BZc+MmRwegcbGRsErdC7Zs2cP303weoiiEBjmgX1/bf3I4R7FyOAgpkCB1uqhpGmERUUxuk9sSjKa6urR1tYmqHrT5jQ2NuLBBx/EQ9/5tiQ8uKzhqL72L4/+H+r7+0XlgMA2O3fuJHsUPMO6oqAo6h0AmQA+oGn6F3aOiwZwhqbpFWy3QcyYcgaVNrVAbzQ4PF6n00Ov12J6aBCZi1JAURTudHVCp9PD11eOkPBwKJX+884Teg4q0wRKZy2dqSIoAQ8uS5gUwapqaIBhUTIpgkXgFVYVBUVRuwDIaJpeR1HUYYqi0mmabrBx+GsA5s9ghNnAvsaxEYfH+vrKMdjaDgWtx3hMDMpOl2AKFIwUDR+aghI0EuPikJyeivComNnz3MlBxTXmE+jSqAis3LRpzt+lUEWQaRGsyKhIr3dA8Lb+ChG2VxQbAZy49/PHAB4AME9RUBT1ZQDjAHpZvr8kMOUM0lZUoPLsObuBfQ3Xb0JTdR2yxHh0GowITIhDRFQM5Ao59Fo9Rvt6Udvbh7aubmQuTsfS5csBuJeDikssJ9A8G8eJ3YOLaRGsHTt2AhCXAwLbHD9+nO8mMELKe2msxlHcMzv9mqbpmxRFbQGQS9P0yxbHKACcBbADwCmapjfauNZ+APsBYO/evSvr6+sBAE888QQyMzNx4MABAMD69etx6NAh5OfnAwACAwNRXl6Oxx57DDU1NQCAY8eOoaysDMXFxQCAp59+GrGxsXjyyScBAJs3b8bBgwexceNMUyIiInDmzBns3r17NuneyZMncezYMZw4MaMHn3vuOSiVSjz77LMAgO3bt+PAgQOzheATEhJw6tQp7NixYzaffllZGQ4fPozTp08DAF588UVMTU3h+eefBzDj3bFnzx7s3DkzOTQ1NeHRAwfwUV0NfCIiEBgSgp27voHKzz5Dg7oGU109iAOFrq4uTN+3FPEPPoj4JBXCwkJx69ZtAEBAQACSkxeh4uw5TLV2IEAO/OCHP8I7P/9XDF+8hIiICBw+fBh1dXV44403AAD79+/H2rVrse9epPfq1avx5ptvIi8vDwaDATKZDJWVlXj88cdx5coVAMCRI0dw+fJlvPXWW249p3/7t3/DxaG7iFmzGvc/8AAqP6sA7o3RRYtScP/99+Po0f8FAPj7B2DXrl1446lnMHnlKiIiInh5TmlpaTh+/Di2bt2KgYEBAMCFCxfwwgsv4Ny5cwCA119/HT09PTh06BD0ej3CVCokbCtA80DfzJiLjML2wu34/e+LodPNeK5997v78OabhxEQMLPwTlgQhuHyctxpaIRcLuf1OXnyfWpoaMCmTZs8/pwAoKioCAUFBbMb6tnZ2Xj33XexYcMGjI+PAwCOHj2KH/3oR6hua4VPWChWrFyFsZER3L5yBYqpafzd3/0dHnroIY8/p6tXr7IWR8G2ovgVgGM0TVfcM0Nl0DT9osUxzwGoo2n6jxRFXbClKCwQXMCdJ1i1ahWOHz9u0yMmYUEIqlpbYUxPxcCYBpRKheS81Tav136zGj49PVjgA4T19uOpffsE9XVqHmxoMjcdOfKOww39a+fPQ1FXjxdEUjZUrVbj1ePHkLqtAHGqRXaPNe9/d1srmkrL8MzuPYJ1QOACIQfcWUsAKaDEnYINuLuGGXNTBYBlAG5ZOeZBAF+mKOqHAJZTFPU2TdN/x3I7JLMMtBfY995778E4NYUNO3fgdtUN3FSr0QIgMXcZ5L6KedeKTk/F1YoKRHV14TvfKRKUkgDmBhs6gxg8uMwhRbCkARNnBGf30oQ6b7GtKE4BuEhRVByAAgC7KYr6BU3TPzMdQNP0rFH23oqCVSXBVWoNPrhw4cLsz5aBfbM1vDMzoFD4ITt/xppfXVODW709CE5IQEh8LGQyOQwGPUa6ejDW2QnZ2BgWyGSzZgFHeHLgWptA9+79jsPzxDaBOlMEy7z/QnZA4BLz90AoMHVGYLqXJvR5i1VFQdP0KEVRGwFsBvAqTdO9AG7aOX4jm/d35JMuNpfKF154AS+99JLVv1n7+s7Oz0O0KgHN1bXoaG5GX0sLjJipTqWkaWQlJiFw8WIMXaty+PXNx8C1NoF++umn2GTh9WSJ2CZQ81gZR6Yn8/4L1QGBa+y9B3zB1BnBhD1nBDHMW6zHUdA0PYQvPJ88BhfLQL45d+6czRfElvkiMjYekbHxuG9yAkN9fTBodZApfBEWFQV//wD093Th7rUqu1/ffA1caxNoa2szAPuKQmwTqHmsjPb+dXaLYJn6b6qvvc0L62vbew/4wHI1zwRb2RDEMm9JIjKb7WWgGHBkvvD3D4C/la9VR1/ffA5cZyZQE2KdQD1ZBIvALuar+ampSYwMDjoMcAXm76WJad6SRM1s0zJwhRPLwCGlEpcuXXLqPhqNBmq1GlVVVVCr1dBoNK40lzGvv/66zb9xUcN7duCmpyFvy2aHWWhl6WkouXyZ1brd69atQ9jUFK5f+AQA8JWv2J9ExTqBMq2vvWH9Bsb1taWKvfeAD6anpzE6psGt6mqUnS7Bx5cqUH71Cj6+VIGy0yW4+umnGOybHyJmuZfmqXmLDUS/omBzGWgLvjaaenp6bP6Ni69vNu2urmKaQN+rqEAlAGVUJJKSkuYdZ15b4yGRTqBMimDVfvopMkMXulwESwrYew/4oKKiAreamhAQFYnIjHRGAa7A3NW8J+YtNhG9ouDapZLPjaZDhw7h0Ucftfl3Ns0XQhq45hPo+ydOQL/3W6xXERQKjuprt5WW4Z0PPxSlImQLR++BJykpKcGl1hYoIiMQkhCPhKzsOX9fEBkBwxIdutT1qG6YSUphLRuC2FzBRa8ouPRJF/pGk+XXN5Ma3ra+voU2cE0T6JkHj0JhZQLdJgCXQbawFyvzadkZSfRRCphMs/5ZWVgNQN3bC71OOy9mSSb3RdKyHLQDqLvdgOi4GASHhs1ZzYstlkb0isIZn3RzHG3qCmGjqaioyOExbNXwFuLATUtLw5NPPonvfe97ggxCYhtrRbCYjAGpIxQZmJtmRwYH0Nzejo6qmzazIcRnZaBxoB8tDU0wjE/MWc1zNW9xhegVhTM+6eY4cqkUgr3elI/IEY7MF0y+voU6cAsKClivIigmmI4BKSMEGViaZiNj45GTnW03G4JM7gtleDgqL1xATlAwHnlg/ew7yNW8xRWiVxRcbOoKxV6/Z88exjlu3K3hLdSB64wMpIi39x8QhgxsBbgC9rMhDNTXYaqlDXlf+9qc1bzYXMEl4R5r6VLpCEculaZBEeuCvX743vl8Yfr6zs3NRVZWFuMBZRq4d5tabLpqWmIauCtFFsNAIDiLLdNsdn4eHty2FVnRMaCam9H3t0voKv8EfX+7BKq5GekLI5CRmoq1a9fOuybb8xaXiH5FAbC7qQsIx16fnZ3t+CAWEWIQmKdlIDS8vf+AMGRgzzRrLxvCUF8fmgYHrZpm2Z63uEQSigJgb1MXEI69/t1332XlOkwR4sD1tAyEhrf3HxCGDJiYZq1lQ6h1YJplc97iEskoCoCdTV2NRoPx8XFo7/Sh9uo1LIyOthmSbwnb9voNGzagvLyclWsxRWgDlw8ZCAlv7z8gDBlwuafAxrzFNZJSFIDrm7qW0dcdw3dx59PLGNZOY1GSal7NaUu42GgyVdDyNEIauHzJQCh4e/8B4ciAS9Osu84oXCM5RWHCGZdKa9HX0fevxd8+/Bh3JiYx0Wc9JN8cseYcsoXQBy6B4Gk8YZo1zVumOjC3b98WxHvHailUDuGskbPR1+lp8x58TUUlbqrV8EmMA+XrB2pgADnpc5WF+aDYlZ/PqilGr9dDLpemLmdaEIkLGQi1ipg1pDwGmCI0GVgrf2rNNOtKWh+W88oJs2Y2h3DSyMbGRrx25AgMdqKvayoqUV1Tg0k/BSa0WgQbdFibvw5BIQs4r4n74x//GL/85S9ZvSbfOPsisCkDoVcRs4YUx4CzcC0DVz4cuBhLHNTfJoqCDYqLi1Ha1oYNRXvtbk7193ShuboWrS0taK2tRYxMjpS0VM4nGCEXlXcFV14EtmTAwUvoEfgeA0JYfXElAzYme7bkY8+yYcIF6wVrikI46zkP40z0tbmf9Kd/eR+orccPt38dS5cuFazJQmjwmWBR6MkdhYi7k6gQFIw92MoKzUZ6GSHklXOE1yoKV7Kl+vsHYOnafDTdHUJgYCDnA//w4cOcXt9TuPMiuCsDMbyE9uBjDLgziXJhkmFbBkL7cBBCXjlHeK2iEEr0tT3q6uqwZs0azu/DNe68CDRNuyUDMbyE9vD0GHB2Eh0fH0d2djamp6dRUVGBqy0tGF+wgNXaLWzKQGgfDkLJK+cISeR6cgWhRF/b44033uD8Hlwz+yKkJjv/IjTcdmsT0917c13qlgmeHAPOlMJNyVmKtslxvPDO2/j5W/+Np9/4NX599q+oGhxAQFw0kjIWI061COExMYhTLcLKTZuwoWgvDIvT8V5FBUpKShi3i00ZCK38qFjyynmtouCi5jRhPu6+CFqtlrd785nckQ+YTqI1FZX4sPQMJiLDYVx2H6ZiY6BLTUFEwVZEP/gV3Bq8iw9Lz6CmonLOeVzWWmeCED8cxGDZALxYUYghW+r+/fs5vwfXuPsi7Nq1i9V7T01N4k5XJzpbW3GnqxNTU5M27+3pKmLW8NQYYDqJmmKLKJUKGYWFSFybh97BuzAsWIDFD6xD0spcLPnaVlAqFW6q1fOUBeD8VzpbMhDih4MYLBuAF+9RAMLMlmqOtdTEYsPdF2HlypWs3HuwrxctDU3o6O7GFCgYKRo+NAUlaCTGxc1J0cJXFTFreGoMMHHu6O/pQnVNDWQq1WxVt8CwhWjQTiEuKBgyuS8AQO6rQHLearRgplZDtCoBkbHxs9dx1sbOlgyE+PUu1DowlnjtigL4IiTf0NCIyrPnbK4stNppVJ49B0NDIwo9mOZ33759HrkPl7hr4vu3f/s3t+995aOPceHCRdT29cGYEIeI3BWIy1uDiNwVMCbEobavb+bvN27MubcQzIueGgNMJtHm6lpM+imQmLts9nda7RQMPnIEhoXMOz4xdxkm/RRorq6d9zdnvtLZkoEQv97FYNkAvHxFAQgvW6rUcDfr5qdupG4ICgqCbHwCtxqbkLR9G9KW5cx+9ZpYEBkBwxIdutT1qG5ogF6n5a2KGJ84mkQnJyfQ0dGO4JSUOSU/9TodQNOQK+crGLmvAsEJCehobsZ9kxPw9w+Y/Rsf5j2hfr0L3bIBePmKwkRhYSGe2rcP21QqKOrq0VRahrqT76OptAyKunpsU6nw1L59HlcSq1dbL9ouNtyp5OWODBobG9EzPQ2/AH9Ab5inJEzI5L5IWpYDn5hYlJ89B587vYJJ7uipMeBo5TfU14cpikJIfOyc308MDkNm0EFh42s7JD4WUxSFob6+Ob935iudLRkI9etd6JYNgKwoZhFittQ333yTl/uyjTtZN92RwaVLl2CMicGXlqSj9nYDWjBjDjH/ItZNT2NydAy6qSmMdvdA03MHsSqVIGIoAM+NAUcrP4NWByMAueIL2Rn0OuhGRxHqp8TE8BAWxsfDEplMDuO9881x5iudTRkI9etd6JYNoigsYCMkny3y8vJQWTnfa0SMuPoiuCoD80CmFevXw9dXgeqaGtzq7UFwQgLkQYEY7evHcP8AJkaHoR28C2psHHK9HtWtLbh58yaWLVvm+EYc48kxYG8SlSl84QNAb+au3KWuxwIjjYVLMtHR2wfDEt28VZvBoIfPvfNNOFu7hU0ZCLGKowkh1YGxhCgKAWMwGPhuAqu48iK4KgNLL57s/DxEqxLQXF2L65/8DX1jYzAq/eAbsgD+QYGIXb4MC2JiMdp7B60X/4aXfvMmvr2tkPc9KU+OAXuTaFhUFJQ0jZGuHgRFRKBLXQ9jbw+yFqcjOi4Gdy9cRJe6HknLcuZcc6SrB0qaRlhU1OzvnP1KZ1sGQv56F6JlAyCKQtDIZDK+m8A6zr4IrsrAmhdPZGw87rR1QhkTiYTMJYheshi+/koEhIbB995mbEhsNGRTE9DTEESCQE+PAXuTaLBSCfVnn0Ez0I8FNIUss0JemYvTUd3QgHYA8VkZkMl9oddpMdbZiazEJPj7B7j8lc6FDIT89Q4Iy7IBeHmacYJ0UavVePX4MaRuK5j1cOnv6cKHpWdAmcUBWDLaP4CBquv48rp8tKrrILvdgKf27RPMnoWnsJbcb2xkFE23biFqVS42P/LwvNLAtTduoO52A8Z95QiMicJoVw/Q2YXVa/MxPTEh2FTuQs906wYkzbjYYTI4H3/8cclsaLuKqzKw5gppigNYkmt772G0rxdK0AgJDxdEgkC+xoCtlV9tbS3Kbt5E441qBG8Mm2PfX7p8OaLjYtCgrsO1jy9goqML8cGBGLpW5dZXOtcysPx612g0UKvVUlQcLkMUhYu4+hXiTBrmK1eucNcBkeCqDCy9eAwGg9U4AHMMeh3Ge/uwNC4OSqU/AHg8S6clfI8By0k0KysL/v7+du37Y00tyA0IRO6WLVi7dq3bk62nZCDGCoiegigKJ3FnMLFVLIXADHMvnsQl6ZiiKERZxAGY06WuR6BOj+T01NnfxaYko6muHm1tbYKyGfOJ0O37rkDeTfuQPQoncKecpiulDlUqFXJycuYd501UV1e7JQOT3IeCAtGvnUbS5q8gcGH4nGMMet2sF09O+hcbtMDMvkbdyffxTzt3ITc31+V2WMJ0Repu/7nGE/Z9rmXAURlSIUD2KDyNO1WxXC2WknDjhqAnCU9w+fJlt2RgegbFJSW4c3cQVFws4jIy4SOjYDTQGO3rxXhvHwJ1+jlePCbYzvPj7IrU3f5zjSe8c7iUgdAKGQkVksKDAc4UdLGWb9/VYilvv/02a30QK2+99Zbb1ygsLMTBxx9Hqt6A8epqDFRdR+/Vaxioug6fzm4sjYrCxo3r5ykJgN08PyUlJXjtyBGUtrVBm5mB1G0FyNq1E6nbCqDNzEBpWxteO3JkTlEfNvovdriUgdAKGQkVsqJggDvlNGNiYqyWOpycnMBQXx8MWh1kCl+ERUXNJk0zpWHWKv2g4WkTVWosW7YM39qxA39pbET2ylWgfABfXzlCwsNnN64tcTaC2B5Cq9NMEE8ZUiFAFIUD3B1MGbW1cyKE+3u60Fxdi46OdkxRFIyYWdYpaRqJiUlIyVmKyNh4xKYkY/Hq1V6/ifrEE0+wdi3T5nZ3Y4NH8/y4Y95gs/9ihSsZMKnBYQ1vdHAgisIB7g6m9vb22QjhmopKVNfUYNJPgeCUFETFx0KuUECv1WKkqwfqzk40t7cjJzsb0aoEBIeECKLKGp9kZmaydi2+8vy4syJls/9ihSsZCLGQkVAhisIB7g4mo9EICjMlJDsHByFTqbDEIoMpAITExEJ/XzY6qm7iplqNhN5eXL92FX6PH2CtL2LkwIEDuHr1KmvX83SeH3dXpK+//jpu3Cuo5K2wPQZMCLGQkVAhisIB7g6mRYsWgf7kE9zo7UX0+vU2U0cAc0tI3rh4EdN3+gVRZU1qeDIOwN0VqdYsWyuBXYRayEiIEEXhAHcH09KlS6HUajGl1SEuh5k9My4nC20ffYzogACv2Syzxfr16zm5rqeydLq7Il1uxRPL2+BqDLhbfdGb3k3iHusAd6tiAcC0ry8CQ4Nxp6GJ0fl3GpoQGBqM3A0boNFoXG67FDh06BCn1zfFAeTm5iIrK4v1l9/dFelPf/pTVtsjRn7+859DrVajqqoKarWa1XfCneqL3gRRFAxwZzC1tbUBC8OwbNkKGHt70H6zGga9zup5Br0O7TerYeztwbJlK/Bx1VVGxeelTH5+Pt9NcAtHJUZtYVqRFhUVcdAqcdDY2Iji4mIszc/Dq8eP4T9OvodXjx/DwVdeRnFx8WyckjuIoQypECCmJwa44y1TVVUFGkDWiuUI7+1F3e0GNA70IzAmCguiYmxGCEdGR+JjmdyrPCukiLvmjU/l3vmKmqfLCf7SA0jdVsBZ7iUhFzISCt45Cl3A1cFkbnowpWFuaWhCR2c3Bjp7YKRo+NAUlKCxNC4OyempCI+KQXdbK2QU5VWeFdYIDAzkuwlu406d5nfeeccDLRQWlsGJd078Yc7+IBfBiVJMdMgmJCmgkzibq0ej0eDgKy9Dm5mBlZs2zf5+amoSI4OD0On0ViOEr50/D0VdPV74yU+9atNMqkg48RyrNDY24rUjR2BwEJxoovLsOdaLS0mokBFJCsgXznrL2DI9KJX+UMYnWL2HyfTQdf4Cgv79F5z2R+g89thjePfdd/luhtu4uiKVSv+ZYi048XTJaWwv3G71eC6KSwmtDKkQIIrCRZwZTK6aHtTDw262UvzU1NTw3QTWsDRv1H9ejfHxcdAGPRZQPvjyfcvwla98Zc6EJ6X+W8P8612v1+NSTTUWLl82Z8U10N9n83xvzb3kaVhXFBRFvQMgE8AHNE3P+xymKCoEwPF799YAeJSmaUlHFbm6GX71r3/1fGNFjBhMBiYlMDU1hU+qrsFIAz5yOYxBAajt7ID/vaykUreFWzPhjgyPoLGxEUujItHf04XI2HhG1/LG3EuehlVFQVHULgAymqbXURR1mKKodJqmGywO2wvgdZqmz1EU9SaArQDeZ7MdQsQV08OSJUt4bjX/HDt2zOExYiphOaf41ZfWY7GDSmpM+i82bFWT62huRrPMB63jGtwtPYOc7Gxk5+fhoYd22L2eN+Ze8jRsryg2Ajhx7+ePATwAYI6ioGn6sNl/IwHYXldKDGc9K8rKypCens5ji/nHkQzEVMLSlVTjzc3NkhoD9mSgN+gR1ZWC0PuyMNLehZtqNQBgUk5hoUVVQnO8MfeSp2FbUQQC6Lr38ygAm59xFEWtBRBG03SFjb/vB7AfAPbu3Yv6+noAMymHMzMzceDATLK89evX49ChQ7OBWYGBgSgvL8djjz02a989duwYysrKUFxcDAB4+umnERsbiyeffBIAsHnzZhw8eBAbN24EAERERODMmTPYvXv3bFDPyZMncezYMZw4MaMHn3vuOSiVSjz77LMAgO3bt+PAgQMoKCgAACQkJODUqVPYsWMHOjs7AcxMekePHsXp06eh1+vxwx/+EGNjY3j77behUCgQ/61vwc/PD6tWrQIwM0n86Ec/wtatWzEwMAAAuHDhAl544QWcO3cOAPD666+jp6dnNoK5qKgIBQUF2LNnDwAgOzsb7777LjZs2IDx8XEAQEVFBZ5++mlcvHgRAHD48GHU1dXhjTfeAADs378fa9euxb59+wAAq1evxptvvom8vDwYDAbIZDJUVlbi8ccfny18f+TIEVy+fHm2yAxbz+m//uu/Zv9v+ZyMRiOWf+1ruDk2ArlCjs7WZixemYua2lrU19UCANasXYOWG5/jB88/D+U//iOKiooYPafDhw/j9OnTAIAXX3wRU1NTeP755wEAjzzyCPbs2YOdO3cCmDETHT9+HFu3bkVvby+0Wi3eeustvPvuu7h69SrkcjmefPJJ/O70abQGKBERoIDP5zeRmpKKv/zl1MyYi4zC9sLtOHHiD9DpdBjo6YL800/x57femu2/kJ8Tk/dp27ZtuN7ejuA1q/AP3/kWrlVVzT6n+x9YD6Nej5b6Oij1OqiW5cBo0OO9k3+Gj38A2lrb8fDDD+NPf/oTxsZGAACPProb165dQ8WHH2Lsk7/hoRW5aGpqYvScvOF9YjORIqvusRRF/QrAMZqmK+6ZoTJomn7RynELAZwF8A2appmEHgvGPdaTrFq1ipOsmWLClgyE4EZp2R575q87d+7g6tQUNhTtZRx0V158FJXvvIPW1lbW28sHxcXFKG1rsyuDq59+itq+PqRtWA+apnHrgzMYvHULT//CuvefSU7bVCqvjmK3gWDdY69hxtxUAWAZgFuWB1AUpcCMeepfGCoJr+Xpp5/muwm8Y0sG7tR4YFtRODJ//aX+NhoqK5GYv8bpVONL1+ZLosoh03TryempaOvqRpe6HknLchCckAD9nTuYnJyYrQBpjrfmXvI0bCuKUwAuUhQVB6AAwG6Kon5B0/TPzI75PoCVAA5SFHUQwJs0Tf+B5XZIgtjYWL6bwDvWZCCkEpZM9h06VMm4NTyEgclJ1FRUIjs/j9G1Y1OS0R4SKglvHqbp1sOjYpC5OB3VDQ1oBxAUHYW7SiWG+vrgbxadzXZxKaEhNA8+VhUFTdOjFEVtBLAZwKs0TfcCuGlxzJsA3mTzvlLlySef9HrTkzUZCKWEJdMSp5QPEJmeCr2vAtU1NYhWJTBy/fRV+OLzmmpJePM4k2596b3U6nW3G9De3IKOW7fRk9PhFbmXhOrBx3ocBU3TQ/jC84lAYB2hlLBkav7y9ZXDh6YQlbEYPaOjaK6uZaQodFodaINeEt48zqZbN+VFu/63y2hobETPxXKMq2sknXtJyB58JDJbwGze7HiTVupYk4EQSlg6Y/4KCQ+HEjQm7t5FcEICOpqbcZ8Nm7s5Pc0tiAsMkkQlNVcKgIVHxSAsLBSpoWF49rv7IJfLBWGG4QJXXKc9qSxIPQoBc/DgQb6bwDvWZOBujQc2Jl6T+SuWgflLqfRHYlwcxnv7EBQdhSmKwlCf/fAhU76vv390tyQmRXcKgP3D3r1Ys2YNZ8Wl+GbWhJmehrwtm21+eCgUfsjbshmy9DSUXL7MSj0OphBFIWBMfujejDUZuFt1kI2JxlnzV3J6KgJ1evS1tMIIwKC1XrzKhMmb51e/+pXbbRUKrhYAk5IMrGEyYa5wwoNvSKnEpXvpXjwBURQEUcJ3CUtnzV8mbx59VzcG2tphtBG/ZFlJTal0bh9GyLhaTU5KMrBk1oSZmuy8B1/DbY+VSiZ7FAImIiKC7ybwji0ZuFN1kA1csbkvXb4cDVVVGG9thfrcRxjo6nKY7+s///M/WWmvUHAl55nUZGCOUDz4HEEUhYA5c+YM303gHXsy4LOEpaslTgOMwHc2b0F0dDSjfF9SHAPO5jyTogxMCMWDzxFEUQiY3bt34+233xZU4I2n2b17N44fP27z73yWsHS1zsjOPXsYF79y1H+x4kwBMKnKABCGBx8TiKIQKI2NjaioqMDBV14WVOCNJzCPSv38888dprBwtuogW7hr/mJS/MqTni184O0ycMWECbDrwccEoigEiCnwhs5aCq3AAm+4xFpU6mhIMA6+8jIj5chHCUs+zV8E8eOqCfNuUwu2seTBxwRWs8dyiCgayQazgTfpaUjNXY6IiMh5x5h/oe7Kz5fE5DMnKvXeZOvnp8Tg3UGM3embnWyFqhy5Sr3Q0dGBxMREVtsqNqQuAw4zIQs2eyzBDSxzB12uuGxVUZgCbyox8yWbkZEhajOUvajU260tWLtpE69RqUzgyvx17NgxPPPMMyy2VHxIXQZ8e/AxgcRRCAjLwBtTURdb8BF4wzaOolJNMuAzKtUZTOYvtqKITYWyvBlvkEFhYSF25edDdrsB5cVHce38eXS3taK/pwvdba24dv48youPQna7gRcrAllRCAQhpc72JHzXlRBaOmeC98KnB58jiKIQCNYCb+5/YL3D8zwdeMMmTJSjpQzYUo5CTedsyXPPPcd3E3jHHRmI7UOALw8+RxBFIRCsBd7IZY4fj6cDb9iESVSqNRm4qxyFnM7ZEimnr2CKKzIQy4eALfjw4LMHURQCwVrgTXn5eaSkpNg9z9OBN2zCJCrVmgzcUY5CT+dsybPPPostW7bwdn8h4KwMxPQhIBaIohAIYgm8YRNPR6UyrUgnNa8yb0JsHwJigXg9CQRrqbPT0tLtnsN26mxPw6SuhDUZOKMcNRoN1Go1qqqq8Kc//Qn9Mpmg0zlbsn37dt7uLRSYykAMdR3ECllRCAjL3EErV660ezzbqbM9DZOoVEsZMI1KtbRRT2l1qPn8c/gtTsXnV64iOT0V4VExdtsnBK+yAwcOePyeQoOpDPj2oJMyZEXhIcy/bNVqtdU88pb5+o/+/qjVa1nm6xfzIHdUV+IPf5ibDI6JciwpKcFrR46gtK1tJgXKtgLEP7AOAfdlIzAnB7V9fbhw4SJqb9xw2L7YlGQMY2bjnQ8KCgp4ua+QYCIDsdR1YDIPCBGyouAYZ70vzHMH9d64jmvp6ZLOHcR2VKotG7VBq4P/ggVIyFoK5YIF6FLXo7qhAcBMnQhbiNmrzJPw7YYq9LoOYvfCIoqCQ1z1vjAF3nz2SBkUAgu84QJ7ifV8dAZcO3+ekXK0t1ktU/jCB4Beq4VM7oukZTloB1B3uwHRcTE2zVB8e5UlJCTwcl+meGICZCIDZ+s6TE5OYKivD/2dXRi+O4S7d++61UZ7SMELiygKjnDX+yItLQ1VVVW8f6l5CltRqSkAFHX1jJSjPRt1WFQUlDSNka4ehMTEAgDiszLQONCPloYmm4qCb6+yU6dO8XJfJnhqAmQiA6YedP09XWiurkVHRzumKArjo2MY7e7Efx8/hpaWFtY/wKTihUUUBQew5Ya5Y8cOnDp1SlCBN1xiLSr1n//5n/Ffp087VI6Oorz9/QOQmJgEdWcn9PdlQ+6rgEzui8CYKHR0diN7ahJKpf+ccyw3zvlQ2qYxIDQ8OQEykQET9/KaikpU19Rg0k+B4JQURMXH4k5zKwIC/KFMSkJpWxurX/ZScscmioID2PK+6Ozs5KqJgsY8KnVsbIzRZMzERp2SsxTN7e3oqLqJ5LzVAIAFUTEY6OzByOAglPFzTRymjfP4+HgUFxfzYl8W4hjw9ATIRAaOPOhqKipxU62GTKXCktxlkPsqYNDr0Kuuw9KsLKy6//7ZfTC2vuyl5IVFvJ5YRizeF1KDiY06MjYeOdnZMLS3o6XyCvQ6LXxkFIwUDZ1OP3ucuVdZlEyGP3788RwPqqxdO5G6rQDazAyUtrXhtSNHUFJS4oFeCgPLLMeO8FQ8ii0Puv6eLlTX1ECWlITkvNWQ+yoAAF3qegTq9EhOTwXAbnyF1OYBsqJgGTa9L8rKythvoMhgKgOmNurs/DwAQHVNDW719sAnKBiGoRGMDt5FN4U5XmWxMhk6jUZe7ctCGwOuZjkOSojF2UufIjk5GQsXLnTKbMdUBrY86JqrazHpp8CS3GUAAINehy51PYy9PchanD5vf4qNL3uhe2E5C1lRsIyz3hcmrLlhHj58mN3GiRCmMmAS5W0iOz8PD27biqzoGIxfv46xK5+h59NP0VQ642W2TaXCN7/8ZfQZDLxH+QptDJgmwFiGE2B/Txcqz55DfW0trtzpxWsn/oBXjx/DwVdeRnFxMSN5OSMDy7oOl8+Uobbmc8hDQjExPIpOdQ0ayy/Cp6cHOenpVl2j2fiyZ3MeEAJEUbAMm/mLTp8+zW7jRAhTGVhLgWKPyNh4rNj4JSRHx+Bba+/H0998BM/s3oMXfvJTFBUVoaurSxDmFaGNAWcmwJqKSnxYegbqO72QpS1G2PJcpGzd4rTZzlkZFBYW4ql9+7BNpcJ05RUMNDZB23sHA1XX4dPZjaVRUdi4cb3d+Bl3Ay09nceMa4jpiWW8MbmfULBMgeKI6xc+QRRN45FHHpljYvDWIlJMYDoBWm4eTwyPYmB6GhHR0YiOT+DcbGfyoEtOTsbYiT8gZdUKRERHIyQ8fJ53mzXc/bKX2jxAVhQs4+yXLWA7ud+LL77IVTNFgzMysEyBYkv+jlKgOGteMcFFug+hjQEmJj5rm8ejfb1QgkZIePjscUzNdu7IYOHChQgNDUVYeDii4xMYKQnA/S97NucBIUAUBQc4yl9kia38RVNTzi1bpYizMmCj9rCQ7MtCGwNMJkDT5nGi2ebxeG8fEuPirE7Ujsx27sjAmb0rc9j4smdrHhACRFFwAFtfts8//7wnmitoXJGBuY1aUVePptIy1J18f85m9VP79tk0dQjJvizEMWBvApycnEBHRzuCExJsuqFa4mjz2B0Z8Pllz9Y8IATIHgVH2MtfJMXkfkLDndrDUrMvs429RI5DfX2YoihExcc6dEM1h0u3UFf2rmx92TsbnS+VeYAoCg6xlb+IaXK/Rx55xIOtFSbuysCV2sNM6mRYwrROhrMIdQzYmgD7O7swPjqGO82t6FXXIVCnR9Zi626o5tgz27krAzYyFLuT/NDdeUAIUDRN890GJoiikfZwJU9QR0cHEhMTPdRCYcKXDBobG/HakSMwOEhTYaLy7DnIbjfgqX37WH3hhT4GLCfQ4btDqO/uRMzy5cjMymJUIAoAutta0VRahmd275mn2NmSwZwkhna+7C1zPVk7zzz5oa3zrOHhfGEUaxciikK4rFq1ClevXuW7GbzCpwxmE9+lpzH6CrW1Oe4OYhkDpgnw7t27+O/jx6BctRL5W7cyPv/a+fNQ1NXjhZ/8dN7EyaYMnF0ZCGEMuAFrioKYnggEG0jFvuwJzE18LS0tM7mxtNO8mu2s4czelZSyv7oLURQCRmqDzRX4lgHf9mW+++8KbG4eA9zIgMnelZSyv7oLMT0RCAzxliJSbCBykw00Gg0OvvIytJkZWLlpE+Pz7JnQTNcV4x4FWVFwBBsDYuvWrThz5gxHLRQHQpKBKx5U7iKk/jsDm2Y7PmTAdvZXUjObMAc2B8TAwABn7RQL3i4DMfefLbMdHzJgMzqf1MwmzEEKA4JAYBN3Ah/5hK3ofFIzmzAHLgbEhQsXuGquaPB2GUil/+6Y7fiQARvR+VLymiK5nlhgdkCwXOTmhRde4KK5osLbZeDt/Qf4kQEbOaKEWjLWFYiiYAGuBsS5c+fYaJ5VNBoN1Go1qqqqoFarBVej1wSXMhAD3t5/gD8ZuJP9ldTMJsxBbEVuxO59QSB4CndyRKnVaknVzCaKwk24LKL++uuvu99AM8S42c62DMSGt/cf4FcGrrr5CqmmCRsQReEmXA6Inp4e9xpnhli9L9iUgRjx9v4D/MvAFTdfIdU0YQOiKNyEywFx6NAhPProo+41EOLOWcOWDMSKt/cfEIYMnHXzlVpNE7KZ7SZ8llpkipS8LwgEPjG5+ebm5iIrK8vm/iKpmU2YA5cDoqioyO32id37gg0ZiBlv7z8gXhmQmtmEOXA1IAoKCtxum2mzPdaFzfbhe+fzCRsyEDPe3n9AvDKQUs1soihYgKsBsWfPHrfbJnbvCzZkIGa8vf+AuGVQWFiIXfn5kN1uQHnxUVw7fx7dba3o7+lCd1srrp0/j/Lio5DdbhBcBl1zyGY2Swi1yI3UvC8IBLHBd00TNiCKgkXYHhDZ2dlut0ns3hdsyEDMeHv/AWnIQKzJEU2QwkUcIaQiN8XFxShta8OGor2MS1OWFx/FNpVKtBuJBAKBvcJFZI+CI5i60dljw4YNrLRFzN4XbMlArHh7/wEiAyFAFIWAGR8fZ+U6Yva+YEsGYsXb+w8QGQgBskfhJQh1s51AIAgfskchYPR6PeRydnW52LLHciEDMeHt/QeIDNyAtT0KoigEzI9//GP88pe/5OTaQtpstweXMhAD3t5/gMjADVhTFERNC5iLFy9ydm13SlN6Ei5lIAa8vf8AkYEQIJvZBAKBQLALURQC5vDhw3w3gXe8XQbe3n+AyEAIEEUhYOrq6vhuAu94uwy8vf8AkYEQIJvZAmbVqlW4evUq383gFW+Xgbf3HyAycAMSmU0gEAgEzyCWFYVXQlHUfpqm3+K7HXzi7TLw9v4DRAZCgKwohM1+vhsgALxdBt7ef4DIgHeIoiAQCASCXYiiIBAIBIJdiKIQNsQuS2Tg7f0HiAx4h2xmEwgEAsEuZEVBIBAIBLsQRUEgEAgEuxBFIQAoinqHoqhLFEX9zMbfQyiKKqMo6hxFUScpilJ4uo1c40gGZsdFUxR13VPt8hRO9P8wRVHbPdUuT8HgHQijKOoDiqIuUhT1G0+3z9shioJnKIraBUBG0/Q6AHEURaVbOWwvgNdpmt4MoBfAVk+2kWsYysDEawD8PdMyz8C0/xRFrQcQQ9P0aY82kGMY9v87AH5P0/R6AMEURa3yaCO9HKIo+GcjgBP3fv4YwAOWB9A0fZim6XP3/hsJoM8zTfMYG+FABgBAUdSXAYxjRllKiY1w0H+KonwB/A+AVoqiHvJc0zzCRjh+/oMAllAUFQogEUC7R1pGAEAUhRAIBNB17+dRANG2DqQoai2AMJqmKzzRMA/iUAb3zG3PAfipB9vlKZiMgSIAtQBeBbCGoqgnPNQ2T8Ck/38DkA7gRwDqAQx5pmkEgCgKIaDBF6aUINh4JhRFLQTwBoDveahdnoSJDH4K4L9omh72VKM8CJP+rwDwFk3TvQB+D2CTh9rmCZj0/0UA/0DT9POYURT7PNQ2AoiiEALX8MVSexmAVssD7n1NnwDwLzRNt3muaR7DoQwAPAjghxRFXQCwnKKotz3TNI/ApP+NAFLu/bwKgJTGAZP+BwDIoShKBiAPXlp6gC9IwB3PUBS1AMBFAB8BKACwG8A3aZr+mdkxj2Pmi+rmvV+9SdP0HzzdVq5gIgOL4y/QNL3Rcy3kFoZjIBjAbzFjlvEF8DBN011WLic6GPZ/DYAjAFQALgPYSdO0hofmeiVEUQgAiqLCAGwG8Mk904LX4e0yIP337v4LHaIoCAQCgWAXskdBIBAIBLsQRUEgEAgEuxBFQSAQCAS7EEVBIBAIBLsQRUEgEAgEu/z/1uyOKWt0eBcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "\n",
    "base_colors = ['#87e8de', '#5cdbd3', '#36cfc9', '#13c2c2']\n",
    "x = np.random.uniform(size=100)\n",
    "y = np.random.uniform(size=100)\n",
    "colors = np.random.randint(0, 4, size=100)\n",
    "#叠加图层\n",
    "for i in range(4):\n",
    "    ax.scatter(x, y, c=base_colors[i],s=200,alpha = 0.17,edgecolors='black',label='class ' + str(i),linewidth=1.5)\n",
    "#调整图例样式\n",
    "ax.legend(edgecolor='none', facecolor='#e6fffb',ncol=4 ,loc=(-0.1,1) ,columnspacing=4)\n",
    "#设置坐标轴\n",
    "ax.set_xticks([0.2*i for i in range(1, 5)])\n",
    "ax.set_yticks([0.2*i for i in range(1, 5)]);\n",
    "#隐藏spines对象\n",
    "ax.spines['left'].set_color('none')\n",
    "ax.spines['top'].set_color('none')\n",
    "ax.spines['right'].set_color('none')\n",
    "ax.spines['bottom'].set_color('none')\n",
    "#设置网格线\n",
    "ax.grid(True, linestyle='--', color='black')\n",
    "ax.set_axisbelow(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 请在今天对应的帖子下回复补充过注释的代码截图"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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